3) advanced_pandas.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=6> <b> 3주차 강의 Notebook </b> </font>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    " 이번 시간에는, 지난 강의에 이어 Pandas의 아래와 같은 고급 기능에 대해 알아본다. <br>\n",
    " 주요 내용: visualization, profiling, join/merge, pivot/pivot_table, apply/applymap, etc.<br>\n",
    "</div>\n",
    "<p style=\"text-align:right;\"> sumyeon@gmail.com </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 필요한 모듈/패키지를 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:09.509407Z",
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    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Appendix의 EDA 자동화 tool용 import. 문제 발생시 무시하고 진행해도 됨 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:09.517403Z",
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     "shell.execute_reply": "2020-09-28T12:11:10.744431Z",
     "shell.execute_reply.started": "2020-09-28T12:11:09.517403Z"
    }
   },
   "outputs": [],
   "source": [
    "# pandas-profiling 설치 필요\n",
    "import os\n",
    "import pandas_profiling\n",
    "import webbrowser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 가끔씩 발생하는 warning message들의 출력을 막는 기능 (Optional) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:10.746419Z",
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     "shell.execute_reply.started": "2020-09-28T12:11:10.746419Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data loading"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-04T11:05:24.016052Z",
     "iopub.status.busy": "2020-08-04T11:05:24.016052Z",
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     "shell.execute_reply": "2020-08-04T11:05:24.020054Z",
     "shell.execute_reply.started": "2020-08-04T11:05:24.016052Z"
    }
   },
   "source": [
    "os.chdir('d:/jupyternotebook/python_anal_atoz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:37.096404Z",
     "iopub.status.busy": "2020-09-28T12:11:37.095409Z",
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    }
   },
   "outputs": [],
   "source": [
    "# read csv file into data frame (main memory)\n",
    "airdf = pd.read_csv('seoul_air_polution.csv')\n",
    "airdf.columns = ['date','loc','NO2','O3','CO','SO2','PM10','PM2.5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:48.366404Z",
     "iopub.status.busy": "2020-09-28T12:11:48.366404Z",
     "iopub.status.idle": "2020-09-28T12:11:48.374405Z",
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     "shell.execute_reply.started": "2020-09-28T12:11:48.366404Z"
    }
   },
   "outputs": [],
   "source": [
    "parkdf = pd.read_csv('seoul_greenpark.tsv', sep='\\t')\n",
    "parkdf.columns = ['loc','total_area','park_area','park_ratio']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 필요없는 Index, Column 삭제"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Index 값을 지정해서 해당 row를 삭제 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:50.364402Z",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
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       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>중구</td>\n",
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       "       date  loc    NO2     O3   CO    SO2  PM10  PM2.5\n",
       "0  20171009  강남구  0.024  0.020  0.4  0.007  28.0   22.0\n",
       "2  20171009   중구  0.031  0.025  0.5  0.005  25.0   18.0"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "airdf.drop(1).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> index 범위를 지정해서 해당 row를 삭제 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:51.699418Z",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>20171007</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>20171007</td>\n",
       "      <td>도산대로</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.018</td>\n",
       "      <td>1.0</td>\n",
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       "         date   loc   NO2     O3   CO    SO2  PM10  PM2.5\n",
       "100  20171007   강남구  0.02  0.018  0.3  0.004   7.0    5.0\n",
       "101  20171007  도산대로  0.03  0.018  1.0  0.004  15.0    6.0"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "airdf.drop(airdf.index[0:100]).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 컬럼을 지정해서 해당 컬럼을 삭제 (= 해당 컬럼을 제외한 나머지 컬럼을 선택) </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:52.753418Z",
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
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      "text/plain": [
       "       date   loc    NO2     O3    SO2  PM10  PM2.5\n",
       "0  20171009   강남구  0.024  0.020  0.007  28.0   22.0\n",
       "1  20171009  한강대로  0.037  0.019  0.006  32.0   16.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.drop('CO', axis=1).head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b> Note:</b> 위의 모든 operation은 원본 dataframe을 변경하지 않음. 필요한 작업 결과는 저장하거나, \"inplace=True\" 옵션을 사용해야함 </b>\n",
    "</div>"
   ]
  },
  {
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   "execution_count": 9,
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    "execution": {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
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      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0\n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filter를 사용하는 Selection 방법"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:54.809414Z",
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     "shell.execute_reply.started": "2020-09-28T12:11:54.809414Z"
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO</th>\n",
       "      <th>NO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.037</td>\n",
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      ],
      "text/plain": [
       "    CO    NO2\n",
       "0  0.4  0.024\n",
       "1  0.5  0.037"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select columns using column name\n",
    "airdf.filter(items=['CO','NO2']).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
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    "execution": {
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     "iopub.status.idle": "2020-09-28T12:11:55.464404Z",
     "shell.execute_reply": "2020-09-28T12:11:55.463404Z",
     "shell.execute_reply.started": "2020-09-28T12:11:55.453406Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>SO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.024</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "     NO2     O3    SO2\n",
       "0  0.024  0.020  0.007\n",
       "1  0.037  0.019  0.006"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.filter(regex='O.+').head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:55.698420Z",
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     "shell.execute_reply": "2020-09-28T12:11:55.708420Z",
     "shell.execute_reply.started": "2020-09-28T12:11:55.698420Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.020</td>\n",
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       "      <th>1</th>\n",
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      "text/plain": [
       "     NO2     O3   CO    SO2\n",
       "0  0.024  0.020  0.4  0.007\n",
       "1  0.037  0.019  0.5  0.006"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.filter(like='O').head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> filter는 Series의 경우 index에, Dataframe의 경우 columns에 적용됨. 필요한 경우, axis=0/1 으로 지정 가능 </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Colum 조건에 따라 새로운 column값 구하는 법"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where문 사용 : if true first value else second value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:57.406404Z",
     "iopub.status.busy": "2020-09-28T12:11:57.406404Z",
     "iopub.status.idle": "2020-09-28T12:11:57.447413Z",
     "shell.execute_reply": "2020-09-28T12:11:57.447413Z",
     "shell.execute_reply.started": "2020-09-28T12:11:57.406404Z"
    }
   },
   "outputs": [],
   "source": [
    "# select columns using column values\n",
    "airdf['district'] = airdf['loc'].where( airdf['loc'].str.contains('구'), other = '별도측정소')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:11:57.843418Z",
     "iopub.status.busy": "2020-09-28T12:11:57.842418Z",
     "iopub.status.idle": "2020-09-28T12:11:57.857410Z",
     "shell.execute_reply": "2020-09-28T12:11:57.857410Z",
     "shell.execute_reply.started": "2020-09-28T12:11:57.843418Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
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       "      <th>1</th>\n",
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       "      <td>32.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
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       "      <td>0.031</td>\n",
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       "      <td>18.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구\n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소\n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구\n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구\n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> Series의 where가 아닌, numpy의 where 도 있음. 전자의 경우 조건이 True인 경우 원래 자신의 값인 반면 np.where는 True/False 모두 값을 지정</b> \n",
    "<pre> np.where(condition, A, B) = A.where(condition, B) </pre>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply/ApplyMap : 함수를 사용해서 조건에 맞는 값을 선택"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 단순한 True/False를 넘어서 복잡한 로직에 따른 값을 선택하고 싶다 </b> </br>\n",
    "<b> 예) PM2.5 값에 따라 4가지 레벨 ('좋음','보통','나쁨','아주나쁨')을 값을 dataframe에 추가 하고 싶다! </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PM2.5값에 따라 4가지 레벨을 리턴하는 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:00.502419Z",
     "iopub.status.busy": "2020-09-28T12:12:00.502419Z",
     "iopub.status.idle": "2020-09-28T12:12:00.508407Z",
     "shell.execute_reply": "2020-09-28T12:12:00.507408Z",
     "shell.execute_reply.started": "2020-09-28T12:12:00.502419Z"
    }
   },
   "outputs": [],
   "source": [
    "def pm25tolevel(value):\n",
    "    \"\"\" PM2.5 값에 따른 4단계 분류 값을 리턴\"\"\"\n",
    "    if value <= 15:\n",
    "        return 'good'\n",
    "    elif value <= 50:\n",
    "        return 'normal'\n",
    "    elif value <= 100:\n",
    "        return 'bad'\n",
    "    else:\n",
    "        return 'verybad'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply 함수 : Series/Dataframe에 적용 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> PM2.5의 값을 레벨로 변환하여 'pm25_level' 컬럼에 저장 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:02.055414Z",
     "iopub.status.busy": "2020-09-28T12:12:02.055414Z",
     "iopub.status.idle": "2020-09-28T12:12:02.066408Z",
     "shell.execute_reply": "2020-09-28T12:12:02.065405Z",
     "shell.execute_reply.started": "2020-09-28T12:12:02.055414Z"
    }
   },
   "outputs": [],
   "source": [
    "# 'PM2.5'가 lambda 함수의 'value'인자로 전달됨\n",
    "airdf['pm25_level'] = airdf['PM2.5'].apply(lambda value : pm25tolevel(value) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:03.448414Z",
     "iopub.status.busy": "2020-09-28T12:12:03.448414Z",
     "iopub.status.idle": "2020-09-28T12:12:03.461404Z",
     "shell.execute_reply": "2020-09-28T12:12:03.460419Z",
     "shell.execute_reply.started": "2020-09-28T12:12:03.448414Z"
    }
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
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       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
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      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal\n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> PM10과 PM2.5의 두 가지 모두 verybad 레벨일 경우 'worst'라는 컬럼에 True로 저장 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:04.630418Z",
     "iopub.status.busy": "2020-09-28T12:12:04.629406Z",
     "iopub.status.idle": "2020-09-28T12:12:04.872423Z",
     "shell.execute_reply": "2020-09-28T12:12:04.871431Z",
     "shell.execute_reply.started": "2020-09-28T12:12:04.630418Z"
    }
   },
   "outputs": [],
   "source": [
    "# 'PM2.5'가 lambda 함수의 'value'인자로 전달됨\n",
    "airdf['worst'] = airdf.apply(lambda row : True if row['PM2.5'] > 100 and row['PM10'] > 150 else False, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:05.520422Z",
     "iopub.status.busy": "2020-09-28T12:12:05.520422Z",
     "iopub.status.idle": "2020-09-28T12:12:05.536416Z",
     "shell.execute_reply": "2020-09-28T12:12:05.535408Z",
     "shell.execute_reply.started": "2020-09-28T12:12:05.520422Z"
    }
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       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구     normal   \n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구     normal   \n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구     normal   \n",
       "\n",
       "   worst  \n",
       "0  False  \n",
       "1  False  \n",
       "2  False  \n",
       "3  False  \n",
       "4  False  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> Apply 기능은, Series일 경우 하나의 개별 value에, Dataframe의 경우 개별 row에 함수를 적용</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:05.975418Z",
     "iopub.status.busy": "2020-09-28T12:12:05.975418Z",
     "iopub.status.idle": "2020-09-28T12:12:05.987419Z",
     "shell.execute_reply": "2020-09-28T12:12:05.987419Z",
     "shell.execute_reply.started": "2020-09-28T12:12:05.975418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    normal\n",
       "1    normal\n",
       "2    normal\n",
       "3    normal\n",
       "4    normal\n",
       "Name: PM2.5, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf['PM2.5'].map(lambda value : pm25tolevel(value) ).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:06.182403Z",
     "iopub.status.busy": "2020-09-28T12:12:06.181404Z",
     "iopub.status.idle": "2020-09-28T12:12:06.228414Z",
     "shell.execute_reply": "2020-09-28T12:12:06.228414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:06.182403Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <th>1</th>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date   loc   NO2    O3    CO   SO2  PM10  PM2.5  district  pm25_level  \\\n",
       "0  True  True  True  True  True  True  True   True      True        True   \n",
       "1  True  True  True  True  True  True  True   True      True        True   \n",
       "2  True  True  True  True  True  True  True   True      True        True   \n",
       "3  True  True  True  True  True  True  True   True      True        True   \n",
       "4  True  True  True  True  True  True  True   True      True        True   \n",
       "\n",
       "   worst  \n",
       "0   True  \n",
       "1   True  \n",
       "2   True  \n",
       "3   True  \n",
       "4   True  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.applymap(lambda value : True).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:06.627418Z",
     "iopub.status.busy": "2020-09-28T12:12:06.627418Z",
     "iopub.status.idle": "2020-09-28T12:12:06.640420Z",
     "shell.execute_reply": "2020-09-28T12:12:06.640420Z",
     "shell.execute_reply.started": "2020-09-28T12:12:06.627418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "\n",
       "   worst  \n",
       "0  False  \n",
       "1  False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> map (for Series), applymap (for Dataframe)은 elementwise로 함수를 적용하고 결과를 전달 </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Frame merge, join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 끝 부분에서, 다음 가설을 확인해 보려 합니다. => 녹지가 많은 지역의 대기 오염도가 낮은 걸 아닐까? </b> (DBMS의 Join) <br>\n",
    "<b> 가설 확인을 위해서, 대기오염 정보 dataframe과 공원녹지 정보 dataframe을 연결 해야함! </b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "      <th> airdf </th>\n",
    "      <th> -------- </th>\n",
    "      <th> parkdf </th>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td> <pre>\n",
    "|    |     date | loc      |   NO2 |    O3 |   CO |   SO2 |   PM10 |   PM2.5 |\n",
    "|---:|---------:|:---------|------:|------:|-----:|------:|-------:|--------:|\n",
    "|  0 | 20171009 | 강남구   | 0.024 | 0.02  |  0.4 | 0.007 |     28 |      22 |\n",
    "|  1 | 20171009 | 한강대로 | 0.037 | 0.019 |  0.5 | 0.006 |     32 |      16 |\n",
    "|  2 | 20171009 | 중구     | 0.031 | 0.025 |  0.5 | 0.005 |     25 |      18 |\n",
    "|  3 | 20171009 | 마포구   | 0.021 | 0.031 |  0.4 | 0.006 |     22 |      19 |\n",
    "|  4 | 20171009 | 광진구   | 0.041 | 0.026 |  0.8 | 0.006 |     25 |      19 |\n",
    "          </pre>\n",
    "      </td> \n",
    "      <td> </td>\n",
    "      <td>\n",
    "      <pre>\n",
    "|    | loc    | total_area   | park_area   |   park_ratio |\n",
    "|---:|:-------|:-------------|:------------|-------------:|\n",
    "|  0 | 합계   | 605,196,566  | 168,843,299 |        27.9  |\n",
    "|  1 | 종로구 | 23,912,883   | 11,555,792  |        48.32 |\n",
    "|  2 | 중구   | 9,960,930    | 3,131,968   |        31.44 |\n",
    "|  3 | 용산구 | 21,866,844   | 1,795,641   |         8.21 |\n",
    "|  4 | 성동구 | 16,860,100   | 3,102,064   |        18.4  |\n",
    " </pre>\n",
    "      </td>\n",
    "    </tr>\n",
    "</table>\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge 명령어로 column(s)을 공유하는 두 dataframe을 병합!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:08.840403Z",
     "iopub.status.busy": "2020-09-28T12:12:08.840403Z",
     "iopub.status.idle": "2020-09-28T12:12:08.874415Z",
     "shell.execute_reply": "2020-09-28T12:12:08.873408Z",
     "shell.execute_reply.started": "2020-09-28T12:12:08.840403Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "      <th>total_area</th>\n",
       "      <th>park_area</th>\n",
       "      <th>park_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>9,960,930</td>\n",
       "      <td>3,131,968</td>\n",
       "      <td>31.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>23,843,621</td>\n",
       "      <td>4,439,901</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>17,063,230</td>\n",
       "      <td>3,363,048</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구     normal   \n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구     normal   \n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구     normal   \n",
       "\n",
       "   worst  total_area  park_area  park_ratio  \n",
       "0  False  39,501,308  6,591,655       16.69  \n",
       "1  False         NaN        NaN         NaN  \n",
       "2  False   9,960,930  3,131,968       31.44  \n",
       "3  False  23,843,621  4,439,901       18.62  \n",
       "4  False  17,063,230  3,363,048       19.71  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.merge(parkdf, on='loc', how='left').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:09.361418Z",
     "iopub.status.busy": "2020-09-28T12:12:09.361418Z",
     "iopub.status.idle": "2020-09-28T12:12:09.386414Z",
     "shell.execute_reply": "2020-09-28T12:12:09.386414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:09.361418Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "      <th>total_area</th>\n",
       "      <th>park_area</th>\n",
       "      <th>park_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20171009</td>\n",
       "      <td>강남구</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20171009</td>\n",
       "      <td>한강대로</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20171009</td>\n",
       "      <td>중구</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>9,960,930</td>\n",
       "      <td>3,131,968</td>\n",
       "      <td>31.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20171009</td>\n",
       "      <td>마포구</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>23,843,621</td>\n",
       "      <td>4,439,901</td>\n",
       "      <td>18.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20171009</td>\n",
       "      <td>광진구</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>17,063,230</td>\n",
       "      <td>3,363,048</td>\n",
       "      <td>19.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date   loc    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "0  20171009   강남구  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "1  20171009  한강대로  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소     normal   \n",
       "2  20171009    중구  0.031  0.025  0.5  0.005  25.0   18.0       중구     normal   \n",
       "3  20171009   마포구  0.021  0.031  0.4  0.006  22.0   19.0      마포구     normal   \n",
       "4  20171009   광진구  0.041  0.026  0.8  0.006  25.0   19.0      광진구     normal   \n",
       "\n",
       "   worst  total_area  park_area  park_ratio  \n",
       "0  False  39,501,308  6,591,655       16.69  \n",
       "1  False         NaN        NaN         NaN  \n",
       "2  False   9,960,930  3,131,968       31.44  \n",
       "3  False  23,843,621  4,439,901       18.62  \n",
       "4  False  17,063,230  3,363,048       19.71  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(airdf, parkdf, on='loc', how='left').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> merge 함수에서 두 Dataframe의 join column이 다른 경우, left_on='loc', right_on='loc'와 같이 직접 join key를 지정해 주면 된다. </b> <br>\n",
    "<b> Tip:</b> join 시에 left, right, outer 등의 방식 선정은 how='left'와 같은 방식으로 지정해 주면 된다. </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join 명령어로 index가 같은 두 dataframe을 병합!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:10.472418Z",
     "iopub.status.busy": "2020-09-28T12:12:10.472418Z",
     "iopub.status.idle": "2020-09-28T12:12:10.504418Z",
     "shell.execute_reply": "2020-09-28T12:12:10.504418Z",
     "shell.execute_reply.started": "2020-09-28T12:12:10.472418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "      <th>total_area</th>\n",
       "      <th>park_area</th>\n",
       "      <th>park_ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171009</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171008</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.005</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171007</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171006</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>20171005</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.004</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "      <td>39,501,308</td>\n",
       "      <td>6,591,655</td>\n",
       "      <td>16.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    NO2     O3   CO    SO2  PM10  PM2.5 district pm25_level  \\\n",
       "loc                                                                        \n",
       "강남구  20171009  0.024  0.020  0.4  0.007  28.0   22.0      강남구     normal   \n",
       "강남구  20171008  0.022  0.020  0.4  0.005  16.0   12.0      강남구       good   \n",
       "강남구  20171007  0.020  0.018  0.3  0.004   7.0    5.0      강남구       good   \n",
       "강남구  20171006  0.013  0.020  0.3  0.004  10.0    5.0      강남구       good   \n",
       "강남구  20171005  0.013  0.021  0.3  0.004  13.0    6.0      강남구       good   \n",
       "\n",
       "     worst  total_area  park_area  park_ratio  \n",
       "loc                                            \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  \n",
       "강남구  False  39,501,308  6,591,655       16.69  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airdf.set_index('loc').join(parkdf.set_index('loc'), how='left').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> join함수는 index가 같은 두 Dataframe을 병합하는데 사용됨\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:11.471406Z",
     "iopub.status.busy": "2020-09-28T12:12:11.471406Z",
     "iopub.status.idle": "2020-09-28T12:12:11.484415Z",
     "shell.execute_reply": "2020-09-28T12:12:11.483413Z",
     "shell.execute_reply.started": "2020-09-28T12:12:11.471406Z"
    }
   },
   "outputs": [],
   "source": [
    "mergedf = airdf.merge(parkdf, on='loc', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pivot/Pivot_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-01T07:25:46.524057Z",
     "iopub.status.busy": "2020-08-01T07:25:46.524057Z",
     "iopub.status.idle": "2020-08-01T07:25:46.531056Z",
     "shell.execute_reply": "2020-08-01T07:25:46.530057Z",
     "shell.execute_reply.started": "2020-08-01T07:25:46.524057Z"
    }
   },
   "source": [
    "<b> 엑셀의 Pivot과 유사한 기능을 제공. </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:12.756404Z",
     "iopub.status.busy": "2020-09-28T12:12:12.756404Z",
     "iopub.status.idle": "2020-09-28T12:12:12.801402Z",
     "shell.execute_reply": "2020-09-28T12:12:12.800402Z",
     "shell.execute_reply.started": "2020-09-28T12:12:12.756404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>loc</th>\n",
       "      <th>강남구</th>\n",
       "      <th>강남대로</th>\n",
       "      <th>강동구</th>\n",
       "      <th>강변북로</th>\n",
       "      <th>강북구</th>\n",
       "      <th>강서구</th>\n",
       "      <th>공항대로</th>\n",
       "      <th>관악구</th>\n",
       "      <th>광진구</th>\n",
       "      <th>구로구</th>\n",
       "      <th>...</th>\n",
       "      <th>정릉로</th>\n",
       "      <th>종로</th>\n",
       "      <th>종로구</th>\n",
       "      <th>중구</th>\n",
       "      <th>중랑구</th>\n",
       "      <th>천호대로</th>\n",
       "      <th>청계천로</th>\n",
       "      <th>한강대로</th>\n",
       "      <th>홍릉로</th>\n",
       "      <th>화랑로</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20161009</th>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161010</th>\n",
       "      <td>29.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161011</th>\n",
       "      <td>48.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161012</th>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161013</th>\n",
       "      <td>45.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "loc        강남구  강남대로   강동구  강변북로   강북구   강서구  공항대로   관악구   광진구   구로구  ...  \\\n",
       "date                                                                  ...   \n",
       "20161009  19.0  25.0  20.0  21.0  12.0  19.0  23.0  23.0  17.0  19.0  ...   \n",
       "20161010  29.0  38.0  32.0  35.0  20.0  31.0  56.0  30.0  24.0  30.0  ...   \n",
       "20161011  48.0  56.0  48.0  52.0  36.0  42.0  70.0  45.0  42.0  48.0  ...   \n",
       "20161012  60.0  68.0  63.0  64.0  49.0  57.0  75.0  54.0  55.0  57.0  ...   \n",
       "20161013  45.0  55.0  54.0  52.0  34.0  52.0  71.0  43.0  41.0  46.0  ...   \n",
       "\n",
       "loc        정릉로    종로   종로구  중구   중랑구  천호대로  청계천로  한강대로   홍릉로   화랑로  \n",
       "date                                                                \n",
       "20161009  22.0  20.0  15.0 NaN  19.0  22.0  16.0  28.0  19.0  21.0  \n",
       "20161010  31.0  25.0  25.0 NaN  29.0  24.0  25.0  40.0  28.0  35.0  \n",
       "20161011  45.0  24.0  44.0 NaN  46.0  33.0  41.0  59.0  41.0  50.0  \n",
       "20161012  60.0  33.0  50.0 NaN  59.0  48.0  48.0  69.0  51.0  63.0  \n",
       "20161013  44.0  31.0  37.0 NaN  46.0  39.0  36.0  60.0  39.0  54.0  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot(airdf, index='date', columns='loc', values='PM10').head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> Pivot table은 pivot의 indx/columns 가 하나 이상의 column일때도 가능. 추가로, pivot은 중복되는 item 항목이 있을 경우 에러 발생 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:13.267418Z",
     "iopub.status.busy": "2020-09-28T12:12:13.267418Z",
     "iopub.status.idle": "2020-09-28T12:12:13.300419Z",
     "shell.execute_reply": "2020-09-28T12:12:13.300419Z",
     "shell.execute_reply.started": "2020-09-28T12:12:13.267418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pm25_level</th>\n",
       "      <th>bad</th>\n",
       "      <th>good</th>\n",
       "      <th>normal</th>\n",
       "      <th>verybad</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>60.833333</td>\n",
       "      <td>10.494949</td>\n",
       "      <td>28.173554</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남대로</th>\n",
       "      <td>63.473684</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>28.391061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강동구</th>\n",
       "      <td>60.875000</td>\n",
       "      <td>10.572727</td>\n",
       "      <td>28.379310</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강변북로</th>\n",
       "      <td>62.560000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>28.217391</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강북구</th>\n",
       "      <td>60.437500</td>\n",
       "      <td>9.669355</td>\n",
       "      <td>27.659193</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 mean                              \n",
       "pm25_level        bad       good     normal verybad\n",
       "loc                                                \n",
       "강남구         60.833333  10.494949  28.173554   107.0\n",
       "강남대로        63.473684  10.800000  28.391061     NaN\n",
       "강동구         60.875000  10.572727  28.379310     NaN\n",
       "강변북로        62.560000  10.500000  28.217391     NaN\n",
       "강북구         60.437500   9.669355  27.659193     NaN"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(airdf, index='loc', columns='pm25_level', values='PM2.5', aggfunc=[np.mean]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-level indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:14.234419Z",
     "iopub.status.busy": "2020-09-28T12:12:14.233418Z",
     "iopub.status.idle": "2020-09-28T12:12:14.246414Z",
     "shell.execute_reply": "2020-09-28T12:12:14.246414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:14.234419Z"
    }
   },
   "outputs": [],
   "source": [
    "mdf = airdf.reset_index().set_index(['date','loc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:14.453418Z",
     "iopub.status.busy": "2020-09-28T12:12:14.453418Z",
     "iopub.status.idle": "2020-09-28T12:12:14.468405Z",
     "shell.execute_reply": "2020-09-28T12:12:14.468405Z",
     "shell.execute_reply.started": "2020-09-28T12:12:14.453418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20171009</th>\n",
       "      <th>강남구</th>\n",
       "      <td>0</td>\n",
       "      <td>0.024</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.007</td>\n",
       "      <td>28.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>강남구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한강대로</th>\n",
       "      <td>1</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>중구</th>\n",
       "      <td>2</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>중구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>마포구</th>\n",
       "      <td>3</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.006</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>마포구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광진구</th>\n",
       "      <td>4</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.006</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>광진구</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               index    NO2     O3   CO    SO2  PM10  PM2.5 district  \\\n",
       "date     loc                                                           \n",
       "20171009 강남구       0  0.024  0.020  0.4  0.007  28.0   22.0      강남구   \n",
       "         한강대로      1  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소   \n",
       "         중구        2  0.031  0.025  0.5  0.005  25.0   18.0       중구   \n",
       "         마포구       3  0.021  0.031  0.4  0.006  22.0   19.0      마포구   \n",
       "         광진구       4  0.041  0.026  0.8  0.006  25.0   19.0      광진구   \n",
       "\n",
       "              pm25_level  worst  \n",
       "date     loc                     \n",
       "20171009 강남구      normal  False  \n",
       "         한강대로     normal  False  \n",
       "         중구       normal  False  \n",
       "         마포구      normal  False  \n",
       "         광진구      normal  False  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:14.674413Z",
     "iopub.status.busy": "2020-09-28T12:12:14.674413Z",
     "iopub.status.idle": "2020-09-28T12:12:14.681404Z",
     "shell.execute_reply": "2020-09-28T12:12:14.680404Z",
     "shell.execute_reply.started": "2020-09-28T12:12:14.674413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([(20171009,  '강남구'),\n",
       "            (20171009, '한강대로'),\n",
       "            (20171009,   '중구'),\n",
       "            (20171009,  '마포구'),\n",
       "            (20171009,  '광진구')],\n",
       "           names=['date', 'loc'])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.index[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-level Index <-> Multi-level Column을 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:16.295418Z",
     "iopub.status.busy": "2020-09-28T12:12:16.295418Z",
     "iopub.status.idle": "2020-09-28T12:12:16.339418Z",
     "shell.execute_reply": "2020-09-28T12:12:16.339418Z",
     "shell.execute_reply.started": "2020-09-28T12:12:16.295418Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">index</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">worst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loc</th>\n",
       "      <th>강남구</th>\n",
       "      <th>강남대로</th>\n",
       "      <th>강동구</th>\n",
       "      <th>강변북로</th>\n",
       "      <th>강북구</th>\n",
       "      <th>강서구</th>\n",
       "      <th>공항대로</th>\n",
       "      <th>관악구</th>\n",
       "      <th>광진구</th>\n",
       "      <th>구로구</th>\n",
       "      <th>...</th>\n",
       "      <th>정릉로</th>\n",
       "      <th>종로</th>\n",
       "      <th>종로구</th>\n",
       "      <th>중구</th>\n",
       "      <th>중랑구</th>\n",
       "      <th>천호대로</th>\n",
       "      <th>청계천로</th>\n",
       "      <th>한강대로</th>\n",
       "      <th>홍릉로</th>\n",
       "      <th>화랑로</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20161009</th>\n",
       "      <td>14264</td>\n",
       "      <td>14250</td>\n",
       "      <td>14245</td>\n",
       "      <td>14237</td>\n",
       "      <td>14268</td>\n",
       "      <td>14267</td>\n",
       "      <td>14248</td>\n",
       "      <td>14270</td>\n",
       "      <td>14247</td>\n",
       "      <td>14269</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161010</th>\n",
       "      <td>14202</td>\n",
       "      <td>14225</td>\n",
       "      <td>14219</td>\n",
       "      <td>14232</td>\n",
       "      <td>14217</td>\n",
       "      <td>14222</td>\n",
       "      <td>14206</td>\n",
       "      <td>14203</td>\n",
       "      <td>14229</td>\n",
       "      <td>14218</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161011</th>\n",
       "      <td>14190</td>\n",
       "      <td>14159</td>\n",
       "      <td>14182</td>\n",
       "      <td>14169</td>\n",
       "      <td>14187</td>\n",
       "      <td>14176</td>\n",
       "      <td>14172</td>\n",
       "      <td>14189</td>\n",
       "      <td>14174</td>\n",
       "      <td>14164</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161012</th>\n",
       "      <td>14147</td>\n",
       "      <td>14134</td>\n",
       "      <td>14137</td>\n",
       "      <td>14140</td>\n",
       "      <td>14121</td>\n",
       "      <td>14118</td>\n",
       "      <td>14138</td>\n",
       "      <td>14145</td>\n",
       "      <td>14127</td>\n",
       "      <td>14136</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20161013</th>\n",
       "      <td>14088</td>\n",
       "      <td>14081</td>\n",
       "      <td>14113</td>\n",
       "      <td>14091</td>\n",
       "      <td>14108</td>\n",
       "      <td>14105</td>\n",
       "      <td>14115</td>\n",
       "      <td>14110</td>\n",
       "      <td>14098</td>\n",
       "      <td>14086</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 390 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          index                                                          \\\n",
       "loc         강남구   강남대로    강동구   강변북로    강북구    강서구   공항대로    관악구    광진구   \n",
       "date                                                                      \n",
       "20161009  14264  14250  14245  14237  14268  14267  14248  14270  14247   \n",
       "20161010  14202  14225  14219  14232  14217  14222  14206  14203  14229   \n",
       "20161011  14190  14159  14182  14169  14187  14176  14172  14189  14174   \n",
       "20161012  14147  14134  14137  14140  14121  14118  14138  14145  14127   \n",
       "20161013  14088  14081  14113  14091  14108  14105  14115  14110  14098   \n",
       "\n",
       "                 ...  worst                                                   \\\n",
       "loc         구로구  ...    정릉로     종로    종로구     중구    중랑구   천호대로   청계천로   한강대로   \n",
       "date             ...                                                           \n",
       "20161009  14269  ...  False  False  False  False  False  False  False  False   \n",
       "20161010  14218  ...  False  False  False  False  False  False  False  False   \n",
       "20161011  14164  ...  False  False  False  False  False  False  False  False   \n",
       "20161012  14136  ...  False  False  False  False  False  False  False  False   \n",
       "20161013  14086  ...  False  False  False  False  False  False  False  False   \n",
       "\n",
       "                        \n",
       "loc         홍릉로    화랑로  \n",
       "date                    \n",
       "20161009  False  False  \n",
       "20161010  False  False  \n",
       "20161011  False  False  \n",
       "20161012  False  False  \n",
       "20161013  False  False  \n",
       "\n",
       "[5 rows x 390 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.unstack().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:16.597419Z",
     "iopub.status.busy": "2020-09-28T12:12:16.597419Z",
     "iopub.status.idle": "2020-09-28T12:12:16.660414Z",
     "shell.execute_reply": "2020-09-28T12:12:16.659418Z",
     "shell.execute_reply.started": "2020-09-28T12:12:16.597419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>NO2</th>\n",
       "      <th>O3</th>\n",
       "      <th>CO</th>\n",
       "      <th>SO2</th>\n",
       "      <th>PM10</th>\n",
       "      <th>PM2.5</th>\n",
       "      <th>district</th>\n",
       "      <th>pm25_level</th>\n",
       "      <th>worst</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>loc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20161009</th>\n",
       "      <th>강남구</th>\n",
       "      <td>14264</td>\n",
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       "      <td>0.004</td>\n",
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       "      <td>강남구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>강남대로</th>\n",
       "      <td>14250</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.005</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강동구</th>\n",
       "      <td>14245</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.003</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>강동구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강변북로</th>\n",
       "      <td>14237</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.003</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강북구</th>\n",
       "      <td>14268</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.002</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>강북구</td>\n",
       "      <td>good</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20171009</th>\n",
       "      <th>천호대로</th>\n",
       "      <td>14</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.005</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>청계천로</th>\n",
       "      <td>17</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.007</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>한강대로</th>\n",
       "      <td>1</td>\n",
       "      <td>0.037</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>32.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>홍릉로</th>\n",
       "      <td>27</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.006</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>화랑로</th>\n",
       "      <td>38</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.004</td>\n",
       "      <td>32.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>별도측정소</td>\n",
       "      <td>normal</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14274 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               index    NO2     O3   CO    SO2  PM10  PM2.5 district  \\\n",
       "date     loc                                                           \n",
       "20161009 강남구   14264  0.013  0.026  0.3  0.004  19.0   10.0      강남구   \n",
       "         강남대로  14250  0.044  0.017  0.5  0.005  25.0   11.0    별도측정소   \n",
       "         강동구   14245  0.012  0.028  0.3  0.003  20.0    9.0      강동구   \n",
       "         강변북로  14237  0.021  0.022  0.3  0.003  21.0    9.0    별도측정소   \n",
       "         강북구   14268  0.013  0.028  0.3  0.002  12.0    6.0      강북구   \n",
       "...              ...    ...    ...  ...    ...   ...    ...      ...   \n",
       "20171009 천호대로     14  0.037  0.012  0.6  0.005  35.0   20.0    별도측정소   \n",
       "         청계천로     17  0.036  0.018  0.5  0.007  30.0   20.0    별도측정소   \n",
       "         한강대로      1  0.037  0.019  0.5  0.006  32.0   16.0    별도측정소   \n",
       "         홍릉로      27  0.041  0.016  0.5  0.006  33.0   22.0    별도측정소   \n",
       "         화랑로      38  0.034  0.009  0.6  0.004  32.0   17.0    별도측정소   \n",
       "\n",
       "              pm25_level  worst  \n",
       "date     loc                     \n",
       "20161009 강남구        good  False  \n",
       "         강남대로       good  False  \n",
       "         강동구        good  False  \n",
       "         강변북로       good  False  \n",
       "         강북구        good  False  \n",
       "...                  ...    ...  \n",
       "20171009 천호대로     normal  False  \n",
       "         청계천로     normal  False  \n",
       "         한강대로     normal  False  \n",
       "         홍릉로      normal  False  \n",
       "         화랑로      normal  False  \n",
       "\n",
       "[14274 rows x 10 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.unstack().stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 각 행정구역의 PM10, PM2.5 트렌드를 그래프로 확인! </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:18.015402Z",
     "iopub.status.busy": "2020-09-28T12:12:18.014437Z",
     "iopub.status.idle": "2020-09-28T12:12:18.031425Z",
     "shell.execute_reply": "2020-09-28T12:12:18.030418Z",
     "shell.execute_reply.started": "2020-09-28T12:12:18.015402Z"
    }
   },
   "outputs": [],
   "source": [
    "graphdf = airdf.groupby('loc').agg(pm25=('PM2.5','mean'), pm10=('PM10','mean'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe에 plot 기능이 추가되어, 간단한 그래프는 직접 Dataframe에서 수행 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-01T09:59:35.155865Z",
     "iopub.status.busy": "2020-08-01T09:59:35.154862Z",
     "iopub.status.idle": "2020-08-01T09:59:35.161867Z",
     "shell.execute_reply": "2020-08-01T09:59:35.160873Z",
     "shell.execute_reply.started": "2020-08-01T09:59:35.155865Z"
    }
   },
   "source": [
    "<b> Dataframe이나 Series에서 plot 함수를 호출 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:19.957403Z",
     "iopub.status.busy": "2020-09-28T12:12:19.956418Z",
     "iopub.status.idle": "2020-09-28T12:12:20.175418Z",
     "shell.execute_reply": "2020-09-28T12:12:20.175418Z",
     "shell.execute_reply.started": "2020-09-28T12:12:19.956418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loc'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tcSD2LJZrxAClp1sbuKmHp6g8Sg1/LNeHgba/IxWnpj2N5alLnUUPpp+lGKy9RM8PxGJqWlzIF5nXib3gAAhHKqYBrpr2NOIasdiMRdzCZNl/mimnc0ctkY8vBWMlZsT7PwFyvnf/1/UG1rYuibz0FKaeH4irTgAR/WltzpI3tbrUOtDeEqA1SJUaaOZA7BE0ZfQz8Qukf3PltGcxV0yPPfNzoQq0uTTqgZ/uBX591L1f11uYT12yNiP2zsMfenYgbqmn2UjntDRAzRXCkykQe3pLNEdnxGo1bXPi1LRn0JR23GbBe4k9S1kmXbjGjTzzcyGxQGu9e5v/VOdSerWhiAqTWEfdzojFymkOxJ6j0qKjljVJEyjtVF/kvjE5QwzEYXb0mRaJ/aaZsiy7aonCubuWxzCZqLFPzFDAP/jMz5v3ErtxVlxxpP1jbzslzh3qCqg6OijyzM+Jvdw5EHuQKluBWIL9xEd/BtZfLe8VsyN9pkXB0UBzrddVD/Y45kIta4GYZ8SKq8mjY1E7rw+LlKictgzEYo0LI4JAqWlrs2EACAgFwvryGrFHqbKyh9iSq+vEggBs+j/g+AagIse517CHOCMOcTAQCyY++EFp1rIZ4W1pag7EyhMbeXSumBaFKtDUw/K9pIoDcQfaCsDQ3HUgBmiduP40oGty27Bc1bMDsa3UdOI4ACrnK6dLDtDWB0DeTf+aciAg3HrqrCvmymlOTytKrIy2PL4yIAwIiODUtCforlALUGZGXHmUmosAPCPuzNqpS52JldM1ebIPRyo9OxBXHQOCooCQGOufDwyn2XLJAedSuIc+a/9Yzq0o2nL7C7VEvJfYM2i6WN+P4KYeHsG8dSnN+udD3Xzwg76ZAkjfCfTeJV7oM9JVMw9LMRIUbJ3aDhz50fnnO6jnBmJDK109dZWWFiVNBPSNjl95GlqBrC/b/62RaUZs1FMw5UDsncQLtM6BOLwvLRtwVaxyBIFmxJH9uz5MJcTNRyFWHaclpbgR9N5Vm+9dx7XKrbtmHiJXD38QBOCbm4Hfn3Tu+U7ouYG4+iT9QneVlhY5u058fCMVQw05h/4t14y4sQqA4FihFsCB2FN0VfHOhz8oT1NKPYu7KtQC3D8jrmibAYuBWDB5XQWwrMyBOKXrx7ja1KPiCP1uJE1y7vlO6LmBWCxysBmIxVaXDq4Ti2npWffQrVxrxFqLAwMcwYHYM1hbIwa4ctoT2FofBtw/IxYLteJGArHD6WMu2GpXWwBABUQkd/2YyP6A2tf5C5iTm+g2dYFzz3eCr+2HeCnxsAdbqem4UYBPgGMz4sYq4MRvdCXdfzrgG9S+TUVqzmxdAjgQe4rOXbVEvJdYeV21trTk03YGuLtmxJUWM2JDW0qaC7ba1ebT345vQNeP8fGjNeTqE859jZOb6TZ1vnPPd0LPnRFX2jkj9vWnrQvl2VQoYY+sLwGTARi3jE78CIuXMRA70cwD4H7TnkJT1rGrlohT08ortbF1SRQS576q6Yocyn4FR7XPiLlgixh0lEHqrlBLFD2Elg4dff/TNwMF6TRBc/Q91wU9NxBXHaOZakQ/249NmkiBtSzLvtc+uJ5SH2lX0L/DEukPVY7mGY72mRaZZ8QciBWjbwZa6tpPXbJknhF7eFe3nqwsEwiOsX6hZCk0jt7UjXp5x9OqBeoK2wNweF/AP6x9G2ZvV38agNB9oZbI2Z7ThbsoE+HG2TDQUwOxyQRU5QIxg6nvsi2OdNgqO0x/wEPOad8WFRpPRRVyrCOJ1diOrhEHhNPFAqemlWPOZlh5o4/g1LSimmsp6CWOoaxWd9y1Tixm8cSe1yoV9cmvzgWMBnm/tjforsd0Z85WTpvT0u5bHwZ6aiCuP03dV2JsrA+LHAnEYpHW2KXt94kpDDnS044e+CBSqWgfIgdi5Wi6KbQLCKOLJQ7EyhCzX92tD4vcVTltLtQa0X5f7HA6AKL2lLxf2xvY08xD5HQg3kLNVAbMcOx5LuqZgdhWj+nOogZRE3FbgdhoADL/RwFu6Lnt94uBWI7KaW0FoPJpTzU7gg9+UJa5z3QXqc/wvp5/4EhPZa6YtiMQu2tGLPaYtgzE4nsYrxM7NyOucqBgS1MGlB+mIOwX5OjoXNIzA7GY4rF2/KE1KhXNimvyul9TPbmJ1oLTllCRl0jcmiLHXmJtOb0RqH0cf25wNK1RclpLGeZA3EU2I7wv/Xx0jW4bEmtjrpjuZuuSyF0z4sq2QCyuEVt+zIHYoquWHTPisATAP9SxvcQnt9Ctm9PSQE8NxOYZsZ2pacC+/cQH19OtZVoaaH+jlbq7liC0tbd0sFBLJFZON9dKNyZmP62tGbG4Tixje1RmXWkmvVGLx+Z1R1xakLtyuuIIFZcGhrffJ04muGCLZsS+gfYt06lUVLBVc5Jqhuyh0Pow0JMDsUrdXjlnD/M6cReBuKkGOPYLXaH2Hd/xc+IbrVbiNWKdFtA3Ob4+LOK9xMrqbo0Y4KYeStE303tE/Gj7ijlD3HACU3MtZdQsZ8MArYf6BvKMGGg//tBWcZ0oejBVQNvz92UyAXlb6G9VLJZzo54ZiCuP0Q+su03fnfW10eoy+xvAqKPZcOdfBPGNVupiLTEV1lVq0xYOxMrqqs+0yLyXmAPxGY7+0r6OK7XyHEAw2rc+DLRnpOScEVu2trSk9qFDDKpO2D+z64ma62gZx55CLZEjBVvlh6kGIHWB/YFeQj0vEDdWA801jqWlAQp2Ef0oEAvCmZ8/+BnNssdcdebngvpQdy7JA7GTFdMiDsTK0pS3ddXqovCDZ8TWaSuBz5cBP98rz+uLZxDbUzENWMyI5QzEViqmRTHDaBdIfaF8X9/T2XPqUmeOBGIF09JATwzEVQ4WallKmkBXRfWnO73mCaB4L/2Qwq2s98nVXctWatMWDsQdNZQ43wjeGZrS7rvz8F5i605tBSDQFiM5Cg0dqZgGKLMWGCFv1XRlFzNiwKJgqxevE9tz6lJnjjT1EAPxIPc28hD1vEBsb2tLa7raT9xVkZal0IS27loSpo+c7TMt4kDc0efLgA/Odk8Vub6lratWN4GY21xal/cn3RpaqNhGamWZgNoPiLUS9LoSEifzjPgIAJX1TJ7YL783rxM7snVJZO+MWNdEHbUSxgChsc6MzmU9LxA7UzEtshaITUbg0OdAQAQw/PyunxuWQG0ypQx65tS0kz1PuWq6XWM1UHKAfj5ialJO5lOzuvnZBYRT5W49p6bNBKE9EAP2t521l9FAfeXjRnTcgmhLaBz97sh1EVeRQ0HGP/jMz5kDcS8+/KG2LTXtyBpxYARdQNkKxAXpVP+jUFoa6NGBeIjjz00cC0DVsXL61FZAUwKMuqT7Td7m7loSbkXhGbF0CtPbP87fKf/XE7eydTcjVqlonZjXiNvV5NHSUFhbtqBU4oumquM007Y3LS0KiQUgyPO3pK2k1+2qWjdqELWr7c3HITqTmgZoVlxXCBhau36MAscedtbzAnHlcVpTDYp0/LkBYbQeU3Kw/QCHg20tLcdd0/1z5eiu5exZxCLzCUwciDsE3wJ3BGIbFdOi8L5UXGjvyV89XV5bU4Wp/6BbqWfEjjTysCRn5XR3hVoAHesXlUozYmuFpL1BXQEd0BEQ5tjzolPpHAAxkFtzcjMdENR/mktDdEXPCsS6RqosdGZ9WJQ0EdA30i99SwNw5Ef6I+g3pfvnydFdS1sO+IUAAaHOPd8/FPDx50AMAAU7qLI9oh9QsEuek7Is2Xt8JZ9L3JGYlh55EaUhy7KkDT6OFmqJQmTsrmWttWVnscOA1gb5jlv1ZCYTzWodnQ0DtteJ64tp7X3gLMe2u0qsZwVi8X92rBPrw6Iki/3EOd/TtgFre4c7k6O7lrbC+T3EAI2Z+03TGnnZYSB5MpAyF2itp3VCOYkXZLbW93kvcTuTETi1DYjsD/RJoWDZVCVt8CnLBKCiZh6OCJWx33SlPYG4F7e61JTSGq4jhVoicYmyq57Tecq1tbTUswKxWN7v6owYoEBsrpa2sne4M6m7a5mM9EfvbFpaFBzNZxIX7AIgAANn0n+A/Olpc59pG4GYtzC1Kz0ItNTTFhKVqn2fr1TpaUGgQByd6niWSe4ZscqnffZmTW8u2HLk1KXObM2IFd4/LOpZgbjKha1LovhRlMI8/isV+AycTVfotphT0xIF4sYqWttwtlBLFBxFKS2DTppxeSMx6A6YSf9Z3icXewMxN/VoJ6alB82j24Q0upWqyr2ugAK9vY08LMm1RiwIFIijB3efGhUDcW8s2HJm65Koz0BqxGStf4DJRAc9hCe5lkWVQA8LxG0zYlf+p/r4UfW0mFq0VaQlCo6ivYlSBWJXu2qJxMrp5l48K87fQT+b5Ml0URWeTFsW5Cx80ZTR9glbx6mJqWnewtR++k3KXLo1B2KJZsTOrg8D8vWbbiihC+Xu0tJA28xO1TtnxI6cutSZbwD9zVubEZcdovfF1PmKtLW01LMCceVxwD+s69Nu7CWmp/1CgBEX2vcclYpmP5IFYnHrkkSBuLeuE7fUUzoyaSLt0VSpKD3dVC3vepu2zL7fQy7WIrom4HQGzVZD2n5nw5Po7G+pArG5YtqDZsT2FGoBdEHXZ2DvDMSuzIgBuohprKD3Aku5ym9bEvna86BLLrkEYWFUNp6cnIxbbrkFDz/8MFQqFYYMGYKVK1dCbc8pJnIyGuiqJyHN9asbMRCPvNixtaTQeNr3KAiuj0HqGXFvDcSFGZTiHzir/b4BM4HMLyg9besN0Bn6FioQs+cNPzCCLvh6e2q6cBcV5IhpaaBtnTiN9vK3ahzfutKZ2Kgn0cGtSwAFQv8w6WfE9hRqiWKHA8c30LJVSIy04/BktQW0hh6e7NzzowcDuX9QelosxgXaMjAqIGWeBIN0jc3o2dpKG6E//fRTfPrpp1i1ahVWrVqFu+++G+vXr4cgCNi0aZPsA7WpNh8w6aXJ9Y+4EJh9H7DgMceeF5ZAY5CiOMrVPcSi3h6IC3bQrVikBbSvE8vV2MO8dcmOGbFKRenp3h6IO68Pi8zp6cOuvb7RAJzeDUQPcT6IhcZK2ycAaJ8R29Nu03w2cS+bFdfmAxHJgI9d88YzWSvYatVQBqbvuPYMjIJsBuKjR4+iubkZN9xwA6677jocPHgQ2dnZmDKF9tXOmTMH6enpNl7FDaokqJgW+QUCZz1JP3xHSNldy9WuWqLeHojzd1JXon5T2++LTqULnIKd8qwTmwu17LyICu9LPx99i/Rj8RZ5f9Ke9/7TO94vVeV0eRad7z1guu3HdiUkjrZTSbkHvSKHvu+oQbYfK25h6k0FW/pmmpQ4m5YGrAfi/J00afKAtDRgR2o6MDAQN954I6644grk5+fj5ptvhiAIULWlXkNCQqDRaGQfqE1SVEy7Sqyc1pYBcHCfYmf2NoSwxdxdqxf2m27VUn/ppAmAf0j7/SoVzYqzv6GWiuIpLVIRsxn21iqIF3yaEvvekHuaxmpavx04+8xey+YZsYtnExfsotv+M5x/jdBYWuZoqpHmcACTiWa3McPsm+3F9MItTHVtRz86U6glshaIPWTbksjmjDglJQUXXXQRVCoVUlJSEBkZierq9tlVY2MjwsPDZR2kXSolqJh2lXlGLEH6SlsBQEVt3VzRm2fEpzPoAPgBM8/83IC2N+T8HdJ/XUePr+ztpzCd2kq3qVaOoIsZStsJXZ0Ri73GB7gQiEMkLtiqKwD0TUDccPse3xtT0+JhD67MiMOTAN/AMwOxfyiQbKNjopvYDMRfffUVnnvuOQBAeXk5tFotZs6ciYyMDADAtm3bMGnSJHlHaY+q47RFxZUfmKukTE1rymgty9l1EVFvDsRikLUs1BKJ98mxn9jcZ9rOGXFv38IkdjfqvD4M0O9//EhK4Rr1zr2+INCMODzJvp4AXQmVuKmHvRXTooAwKljqVYE4n26daeYhUqupTXH1SfpdqCsEqk9QBsaRE7hkZDMQL1myBBqNBkuXLsU999yDf//733jsscewevVqXHXVVdDr9Vi0aJE7xto1QaBAHDWI9gErRcqDH7QVrhdqAbT9A+idgbhgJ23mt1wfFsUOp4uUAhnqG8wnL9k7I+7FTT0EATj5J1WPJ46z/piENKqoFutAHFWdS2u7/ae7tpshROI2l+aK6S5OXbImdigtYXTeitNTmbcupbj2OjGDqUZAW96+X91D0tKAHWvE/v7+ePnll8+4f+3atbIMyCmaMtoUP2iusuOQ6uAHXSOg00gTiP2D6WSR3haIdU10nGXiWCDQytKJSkVvzEd/ovSXK2tQndnbZ1rUm/cS156ig1pGXAiofaw/xrJgK36U419DvNhypVALkG9GHGtnalp87MnN1Ds52QMykXKrkyA1DbSvE1ed8IhjDzvrGQ09zBXTyrYpQ3A0Vei6ukYsVTMPUW/sN120m6oiraWlRXKlp7XlNMOzdsi7NZ62RiwI9Gaft1X+1qhdbVuy5GqHrUIJCrUA6deIK44AfsGOpV3FYtTecvhDbT6t5YpFp84yB+Jj9DsX0V/6Ik0XuLgA6SGk3LrkCrWagqerBz+Yi31c3LokCo6y3mu1JxP3CA/oJhBb9p0et0y6r60ptX82DABBfShr0VAk3RicVXUC+OkeIH87/ds/DEidBww5Bxh8NhDuYte6zsyB2Eqhlih+FACV85XTBelAYKRjM09rQiVsc2k00PtW/Ch637CX+RSmXrBOLAht2aqBrjdIEgNx1leU1h95ieJtLS31jEAs/lLGKhyIAQrE5Ydd665V0xY0o1xcFxEFR9ObmL7Zdu/jnqJgJwBV94d9x48CAiKkbexhaLW/q5ZIpaJTmJScEetbgB2vAjteofXYoefSG+DxjXQm95Ef6XEJYygoD11EHei6SifbQzz2MKJf99u2AsLo86WZjv9d1RdTenPoeY4FPGuknBHX5NH/Z0fWh4HedQpTUw0t0blSqCUSA7GYHfGgtDTQUwKxuIc4eoiy4wCoUrZkP70ZO5tOEc/OlOr7MVdO17Qfu9eT6VuAor2U0gyK7Ppxah9aNzz+KwVBMUXsCntPXeosvC8VFRla3X9A+altNAuuzgXC+gKLXwCGX0AB77zngapc4MRvwImNdNFSlglsf4lm8oMXAsPOA0Ze6nigK8ukvxPxa3UnIQ3I+Q6oLwIi+9n/NcQ3XlfXhwFqd+sXLM0asSOtLS0FR1HRWG9ITdfl060UO2GCo6hwtbmGCjiVrifqpIesEZ+gsn5HzxiVg1gp68rhD+J+txipA3EvKdgq3gsYW7tfHxaJ+0qlqp52thGLKwVbznYHa6wCvr0F+PhCmqFNvRW4YzcVTlkGxpjBwPTbgOu+Bx7KB65eD0xYQen0rC+Br24Atjzr+Nc/2c22pc6cXScWf66urg+LQmKlqZp2pLVlZ7HDaQuOrsn1cXgyc8W0RIWU4qw4aSJdRHoQ7w/ELfW0JucJaWmgU3ctJ1WdoJRpiATde4DeF4jN68NWGnl0Jq4hS9XYw9GKaZGzBVsnNwPPJgBvTgW+ux3Y8wEdPNLdnltBAPZ/CrwxCTj0GVWW37QJOO852wcrBIQCw88HLnoduDcHuGUH7c3d8Wr7oQr2EteHU+yYnYgHNTgaiAt30SzWmYMerAmNo0BsMrn2OhU5dOvMoSOxwwAItBe2J3P11KXOxEDsYWlpoCekpsU0rtIV0yJXu2sZDTQ7SRwjXTGBmCLvLWcSi4VG9nRRShxLpx9JVTntdGrayRnx7vcBQwtQd5rSlQfbthX6BtL3ljSJWnwmTaQ3tKrjlIYu2EnVqOc+B0y+2bnGMeLpSBe/SbPq724D/r6VerXbom8GCv8C4tPsaxfpTKvLphoKeClzpGvcEBIHmAxAS51rlbwVR+li25nlEHOry+PSXWB4Iim6alnqPw3I+h8thXiYHhCIxdaWHjIjdrW7Vl0BbbuRcr3bco24pzO0AkV7gLhR9r1R+vgC/afSzFJb6XoPYZcDsQNNPZrrgNzf6Xu9ZTsV8BTvo9R88T5aJz+d0f744GigpYF+v4ZfAJz3gjQ1AylzgCl/B3a/C/y5Cjj7advPKfyLlg/sXasLjacMkSOBWPzepUpLAxaV0xXOB2JDKy0/JU9y7mLbXLDVw9eJzV21XOiGZmn8clp2cXUrlAy8PxCL1YNKb10SiXt/ne2uZV4fHizNeIDelZou3k8zxIF2pKVFA2ZSIC7YCYy6xLWv70qxFuBYID7yI1Xepl1OhWfxI+m/Ccvp87pGqjQWg3PRPlobO/v/KL0spYVPUUFX+usU5PtN7v7x9mxbsiTOvk9upguQ7orwRFI18rDUoXLaye1QVSeoB7qzZ2GLgbinn8JUV0BLPFLt9FCrPTIIAz0hEPuHUIon3sXTjqQi9hd2dkYsdcU00LsCsfn8YTsKtUSWjT1cDcTmc6TdkJo+/BXdjr7c+uf9QygISRmIuuIfAlz8FvDfxcB3t9IMvbs3UPHYQ0fGJgbi8sP2/XwLd1GDnWQbFwWOkKK7ljiTdaZQC6CL/cAIz9jC1FgNfHYVnUoVnkRb0SKS6ESx8GT6OCTO8Yp6o4GWW3pD9zD0hEA85wFgxp2esz82JIbK451dIzY3J5EyEPeiftOOFGqJ+o6nNVUpKqc1ZXRhaG9XLVFwFI3B3hmxppy2HSVPVvagE0sDZ1LldcYaqqI+5xnrj2uqoYKygbM6Hk9pi2WrS1uBWNdIR2AmjnXsa9giRb9pVwq1AMoOxA6npQeDTtmDC3K+o6UgqLou1lP7UcYnoh91s5q4gmoWutNQTFkDT/ndlpn3B2KVynOCMEApQle6a1XnAlBJey5tbzn4wagHTu+mN6kQB46P9A2ggJa/g4KEK+krTZlzZ0irVPRmZe+MOOc7moWMXuL415LTWU/SfuP0N4DhF9L6e2entgIQHN/LKQbiUjvWiYv2UlFVf4mzAVLMiM2nLjnYzMNSzFBaA6/Js/8YRTkc20C3dx2k6vT609REpb6Igqnlvwt2UsZq/8e0W2HmP6lbm7XZshSnLnkR7w/Enig0ntJPznTXqjpBDQukvLjwC6QK2Z4eiEsOAvpGx2bDooGzqNq6cJfz66eGVqpMT3BymSQ8iS4G7JnlZH1FmZdRlzr3teTiHwxcsgb48Ny2FPWOM7MDjq4Pi6JTae+yPVuYzI08JCzUAqTprlVxhJaLXCkMNLe6PKpcIG7V0kVV/Oj2mWtoXNezXYOOAnH66raajB2Unp9xJ5B2Rcffeam3Lnk4799H7InCEqhgyNGjylrq6Q9cjg5hwVE9v2ravD7sRCCWorGHuVDLyX7M4UkABNv1BbX5dKhFyhz7j1p0p/7TgOm3U6vWzf868/N5f1L6vqtjD7ui9qG2pJVHbR9GYW7kIfWM2MV+07om+vm5MhsGLAq2nDwaUgp5W9rbodrD15/28C7/li7QxlxF4//+NuC1McCO/7S/Z5pPXeodM2IOxHIwb2FyMD1dJXFHLUvB0TQjdrYLU1eaa4Hsb6V/XWfYc9BDV5InU/GQK409xEp5Z0/Nsrepx+Gv6dbT0tKWFjxODRT+WtOxl3fNKQpEKbOd27ucOIa2X3W3dceop3XL2BHSV8kGhAM+Ac7PiKuOARCcXx8WecIWJjEtPWyx489NSAMuexe46xAw7XagVQP8sRJ4ZRSw8THa/QDwjJi5wNnuWmKnnGgJty6JgqNplq6XuC3exseAL69v/6NUitFAe1OjBzs3S/QLopRaWabzh66LM1mnZ8R2bmHK+pouGkZc6NzXcQe/IOCSt2lp5vvbqHgKaFsfhn1tLa2xp7FHaSb9nstRLa5SUfrV2RmxM2cQWxOeTI1olKqcNhmpR3toPBU7OiuyH3Duv4F7DgNnraRljF1v0Gzbx9/5vyUvw4FYDs521zJ3CZNpRgxIu07cVNM+O8v6UrrXdUbZITqpxZn1YdGAGVQAdXq3c88Xf97OpovtaepRcQSoyKYiF3v20iqp32Ra/6vNB/54iu6z5/zh7lhWTnelUOL+0p2FxNKM2JkskLli2sXUtFpN7xNVJygoulvRXnovGbrI9VOtANrfPvte4O4s4KLVtO484iLXTvfyIhyI5eBsdy3zjNhLAvHBdTTLBmhG3KqV7rUdJaY/B852/jXEIO5setrVGbHY5aq71HRW297htC72DnuaeY9SS8bd71IQzttKFxzOZn3iRlKRWneBuEDCE5esCY2ntVFnMicVbalkKQqsYodTdzKxsMmdjv1Ct86kpbvjGwBMuA64dSew5ANpX9uDcSCWg7PdtapyKd0kxXF8nZn3EktUsGUy0QEDvoHU3tDQ3P7HqQSxV7QzhVqiflMBlY/zfaedPXlJZGtGLAjUxMMvhM7X9QZ+gcCla+j/6/+uo6ryQfOd76PuH0xBvCzL+ozUZKIZcUR/aiohB7Fgy5m9xBVH6EJNitN/xLa+ShRsHdtAFez2HNjBbOJALAdzdy0H1ohNJqoyjU6V7rAHS1L3m87bDNSeooKhyTfTfUqlp01GmgX1SXHtIiYgFOg7jhpBiGuajnD25CVRcDQVAtV3EYiL99PsZ/hixxuGKClpIjDzrvYZpLNpaVHCGKC1wfpMsOoYFRDK2U0sxMm9xC31QEOR64VaIsstTO5UfZL+P6fO967fQw/GgVgOIbEAVI4F4vrTlOaVY30YkD41vactbTT5RroyTxhDewOV2CJVfhhorXdtNiwaMJMaQTizTqwpd66rlshWUw/xQseTq6W7Mu9hSiur/Vw/lL27s4nl2rZkKdTJvcRiYZWzrS07MwdiNxdsHf+Vbu3dtsRs4kAsBx/ftspKBwKxnOvDgLSBuK6Q/hj7TqAj9gAgbQkFsJzvXH99R7mybakzy77TjtKUur6vNzyJUtydzxM2GYHsb4DASI88T9Um3wBgxY/ATX+0BzJndReI5WrkYSnEyb3Erra27CxyAFUWuzsQizskOBBLhgOxXELjaUZsb2WlnHuIAWnbXO77L1UXT76p/T7x4AGxmMidpFgfFvWbCkDleGMPsauWs+vDovC+oKYenS7i8ndQgB51ibK9hV0REkOpf1d1OyPeRRedcp7G5uyMuDybbqUKxD6+dOFeddx9+/iba+lvI2mSZzaT8VIciOUSlkB7GVs19j2+WsatS4B0M2JDK7D/E5qZjb6s/f6IZNouUpBOfWXdxWSiQBzRX5pzS4Mi6Y2+aC+gb7H/eeZmHlIEYpxZsGU+ackL09JSC42j/8+dA3FdIa3B9p8uT52FyJk1YkGgLJJ/mLQnxcUOA3Rax47PdMWJP+gwhmE8G5YSB2K5iDMjeyunq2Rs5gFIdwLTkR+pWnT8tWf2w05bAkAADn/j2tdwROURukqXYjYsGjCTtoUU77X/Oc6eQ9yZWOlr+cZq0AE5P1ARoJwpV2+SOIaCrmVNQoEb0tKAc1XTpQfpQmHYuVRJLhV3d9iSa9tSL8eBWC7izMjevcTVubQ+KOWRbZZ8/KiQqLHKtdcRi7Qm3XDm50ZeQue/HnZjejp3E906cv6wLQOd2E8sVSC21uby5CagpQ4YdVmvaXBgk7UOW4VuKNQCKBvk4+/YjDjne7odebG0YxEDsT0HYbjKoANy/6DMk6sNSVgHHIjlIq6f2NNdS9dIMyC5ZsOiuBE0gzz+m3PPL8+mN7vUBbTNqrOQaPpc6aH2Gb6cdE3ArjdpX+2QRdK97oCZtD96/6eUireH1IHYcguTtzXxcAdr68QFu+iUMbH7llxUqvbuWvYQBCD7O/o9HbxQ2rGIv6t7PrD/d9VZhem0bWzYYnlT/70QB2K5mPcS2zEjrpa5UEt0/kt0Jf/9bc71yjVvWbqp68eIa5juKNra/S5Vpk+71bUj5ToLjqLvsaGICtPsIVbIu9obt3NTD10jpQP7pFCVOiOdW102VtHe1uTJzh0m4aiQWPobsqdIqiyL9twPXST92emhcfS7Wn+aajfkxNXSsuFALJdQB9aIq2TeuiRKSAMWPkVrW9/f7lilZUsDkPkFNZvvbvY5fDF13Dn8lbyVnC31wI5XKU04407pX3/WPTS72vYSzbxtEWfEzp68JAqOoYslMTV9bAMV/aUt4VmIpT4p9PMpbUtNu2PbkqXQOOomp7OjratcaWnRrHtotr3tRft+V50hCHRBGBDuWj93ZhUHYrk4chSieUYsc2oaAKbeSp2NTmwE9rxv//Myv6A3nUnXdz/jCAijgpTqXCpQkUv6G7RuOutueQ4/CImhmXZjBc28bZEqNa1W06xaDMRZXC1tlVpN1cdVxwF9c3uhltzrwyJ7K6cFgfbW+wYBQ86WaSxtv6vacmCvTP2ZK3Ko2GzwQu/dPufBOBDLJTQOdnfXcteMGKA3sEvepl63vz1uXzMAQaC0tNoPGH+d7cenXUG3cqWntZW0NhwaT32u5TL9DiAwAtj5H8oIdEdTRrMFKYrtwpMo1d1YRcUx8WnSHBLQ0ySk0VaaiiO0fqn2A5Inuedr21s5XZFDF6VDzpavEBMAZtxBxZg7XrV/y6QjzGcPe0mPcy/DgVguPn60d9ee7lrVJ6jgIqKf/OMCgPBEOmrM0AJ8faPtIo+CdCryGnmRfZv4By+kAHb4G9rnK7UdrwD6RmDOA/K+uQVFAjP+Sduj/nqr+8dqSl2fDYsikqhhyu53AZOei7S6IhZsFf5FKeqkCdKvwXbFPCO2sfQkpqVHXSLrcBDUh5ZomqqBjLelf/1jG+jgDqmLzRgADsTyCku0PSMWBGqiHpUqzbme9hpxIR03VpYFbP5X948VU9jdFWlZ8g2g19eUtG8pkUrdaRpPZH9gwgppX9uaqbfQuu2uN7vuoy121XJ1fVgkVk7vfo9uR3MgtkoMxHs/pJmxu9LSQHt3LVup6Zzv6SJ7yDnyj2naLdRBb+dquniUiqac9tQPmNHej4BJigOxnMLiaV21u3N6NaX0GHesD3e2aBVdAKSvbj+wvTNNOXDkB9o36MgbnVzp6W0v0Fmw8x51z1pVQCgdWN7aAOx8zfpjzMcfulgxLRIrp5trqOWmFB3DeqK4kTRLE7vSubPZSYgdqemKo9RoY/BCqp2QW0AYFW611tOFo1RObKRbTkvLhgOxnOzpruXO9eHOAkKBy9+jJhzf3mp9xnfgEzrMYfKNjlXtDpxNM8Sc76gRgBSqcoED6+ig+TFXSvOa9ph0AwXZjHes7wsX75Oq967lUY5cpNU1v8D2hhZQtfUJdxN7ZsRHfqBbuaqlrZl8E/3d/bXG9eY9It62JDsOxHKyp7uWeKi33HuIu5I0EZj3CKWRf7yr45YjowHY+1/aJjLmKsdeV+1DnaCaa4G8LdKMdcuzlIJc8Lh7O0z5BdF6tKGZimE6E3++Us+IVWr51xa9nZiejh8lT/V8V8Q14u5mxNnf0VY0dwYw/2Bg9v2UZbP2u+ooXRNwcgtd/Fpr4sMkwYFYTvZsYRK3LikxIxbNuocObDjyA3BwXfv9JzZSU4sxVzmXWkuTsLlHaSYdA5g4jtaf3W38ckoR7/3gzEMtzKlpiYq1IgdQliJ1getHBvZ0YiB25/owQMVRKp+uZ8RVJ4CKbCD1LCAw3L1jm7iC9vvveR9osLPFbldObaULUE5Ly4oDsZwcSU0rsUYsUvsAl71D229+eZCKxwCLIq0bnXvdpIlAn4HA0Z9dbzSw+Rm6PetJZRpb+PoDcx+m9eltL3b8nDgjdvXkJVFINHD9z8Ala6R5vZ5s+Pm0n3jcMvd+XbW6+zaXcjfx6I5vADD3QdoVsf1l116Lty25BQdiOdmTmq4+QWmuwAj3jKkrkf2B89u2BX3zd9pffHIzzZTjRzn3mioVrXHqG4HjG5wfW+FfNDsfOJtmiUoZcxVlLg6sBWry2u/XSDwjBoD+03g2bI+oQcCtO2nrkruFxnbdKjbne9rXrNRxgeOWUfexff+lRhzOMJno6MbgaGodymTDgVhO5tR0FzNifTNtx1FqfbizMVcAaVfSVoVPLqH7nJ0Ni1ytnhYEYNP/0ccLnlC2zaOPLzD/ESpe+/P59vvNa8QSBmLm+ULi6CJT19jx/po8OhVq0DxKYSvBx49qP0x6YOsLzr1GyQHK5g09l0/9khkHYjmJ+0q7mhHX5AEQ5D91yRHnvwRE9KfirZBYYMRFrr1e3HBKHZ743bm9jSc3AQU76c2gvxurYrsy8lL6fjK/oO0pgLRdtZj36Kpy2l1NPGxJWwLEDgcOrm9fbnLEcU5LuwsHYjn5+tMG+67WiM3rwx4yIwYoRX7Zu9SEYNqt0uzVHX05XZkf+dGx53WYDT/u+jikoFYD8x8DIAB//pvu05ZJ18yDeY+u9hLnfE/FdsMWu39MltQ+wPxHaafBn885/vxjG6jqe9B86cfGOuBALLewxK5T09UK7iHuzoDpwIN5wKx7pXk9sTNU1peOPe/ID3S28egl7dWxnmDYeVSIlvM9ULSX2gpyWrr3sTYjri2glG7KHM/oQjX8QjoyMutL6sltr7pCoPwwkDKX+g0wWXEglltYPHW6sVY1XOWmc4id4R8i3XpsnwHUbOHUdvsOwQBoD/PmZ2iLyPxHpRmHVFSq9hn6T3fTLQfi3se8l9giECvRxKM7anXb76oAbPm3/c879ivdclraLTgQy818LrGVAFR9giorIwe4d0xKSLsCgEAHQdgj8wtqdjL+Ws9sJDBoPjBgVvvB9ByIex/xBCbLyuns7+jicfgFigzJqiHnUNXzkR+AkoNnfl4Q6AK5YBdw8DMK2OLBEdxNyy26OViWScKyqUfUoPb7BYFmxFEp3Z/v21OMvATY8BB1x9r7Aa09qX2pulPt13bb9m8ff+B0BuATAMx9SOmRWyfOij9qe6OSqqsW8x6dZ8R1p2nHQcpcOiPYU4i/q59cDGx8jGbrtaeA2nygpu3W0Hzm8wbNo5PAmOx6QQRQWFfdtRorKWU9cJb7x6SE0Fg6ySj7W6ClHjDqaRuQUU+FXIKV4xJn3evZbwQDplND/9w/uFirN+q8RiwWI3pKWtpSylzah5+/HSjY0X5/QDgtjfUZSJOCPgNp/3GfgXzYiBtxIJZbV921PKGjlrud+2/6zxqTiQKyUUfBWRA8o9jFlsUv0elVQxcpPRLmbsHR1A9crJrO+R6ASpkWrLaoVMDFb9IhLOFJFGyjUtpadSq4N58B4EAsv666a3lqxbRS1GpAHUDt+bxJVApwwStKj4IpQe1DwVhbATSUAKf/oroBT+2I1mcAMPMupUfBrOBiLbmJR+N13sLkiXuIGWOOCYmjGbGYlla6iQfzShyI5dbljNgDTl1ijLkmNBZobaAqf6g8q1qaeQ0OxHLzCwQCI62vEQf1oZN2GGPeSaycLt5HB3WEc/U8cxwHYncIS+xYNW3Q0ZYBng0z5t0s14M9sVqaeQW7AnF1dTXmzp2LkydPoqCgAEuXLsWyZcuwcuVKmExWtp2wjsLigZY6Om0JoCAsGHl9mDFvJ/abBlw/IIX1WjYDsV6vx5NPPonAwEAAwKpVq3D33Xdj/fr1EAQBmzZtkn2QXi+00xamai7UYqxHEGfEyVM8e88782g2A/Hzzz+Pq6++GnFx9AuXnZ2NKVOmAADmzJmD9PR0eUfYE3Ru6lHFW5cY6xHiR9Ht+GuUHQfzat0G4m+++QZRUVGYPXu2+T5BEKBq2wAeEhICjUYj7wh7gs6BmGfEjPUMiWOB+3OBCSuUHgnzYt029Pj666+hUqmwa9cuHDlyBA899BBqamrMn29sbER4eLjsg/R6nbtrVeVSY/g+KcqNiTEmjdBY249hrBvdBuJ169aZP16+fDmeeuopvPjii8jIyMDUqVOxbds2TJs2TfZBer3Oe4mrT1CXG19/5cbEGGPMIzi8femhhx7C6tWrcdVVV0Gv12PRIu6xa5Nld62mGjpInteHGWOMwYFe059++qn547Vr18oymB7L8kxibm3JGGPMAjf0cAf/YCAggoq1zIc99KJTlxhjjHWJA7G7hMVTIOYZMWOMMQsciN0lLAForgHKs+nfvEbMGGMMHIjdR1wnPp0BBIR77pmljDHG3IoDsbuIe4lbG2h9uK0pCmOMsd6NA7G7iIEY4PVhxhhjZhyI3SU0vv1jXh9mjDHWhgOxu4RZHBgew1uXGGOMEQ7E7mKZmuYZMWOMsTYciN3FnJpWAdGpig6FMcaY57C7xSVzUUAoEBgJBEYAfkFKj4YxxpiH4EDsTpe9y0GYMcZYBypBEASlB8EYY4z1VrxGzBhjjCmIAzFjjDGmIA7EjDHGmII4EDPGGGMK4kDMGGOMKYgDMWOMMaYgDsSMMcaYgjgQM8YYYwriQMwYY4wpiAMxY4wxpiAOxIwxxpiCOBAzxhhjCuJAzBhjjCmIAzFjjDGmIA7EjDHGmII4EDPGGGMK4kDMGGOMKYgDMWOMMaYgDsSMMcaYgjgQM8YYYwriQMwYY4wpiAMxY26iN5rQrDMqPQzGmIfhQMyYm9zzxUEsfGUrWg0cjBlj7TgQe7my+hb8b+9pCIKg9FBYN0wmAVuPV6K4rhk7TlQpPRzGmAfhQOzlXtt0Ag9+lYmtxyuVHgrrRkFNEzQtBgDAz5mlCo+GMeZJOBB7uYOn6wAAG7LKlB0I61ZmUZ35499zytGi5/Q0Y4xwIPZizTojjpdrAAC/5ZTBYDQpPCLWlayiegDA+P6R0LQasJ3T04yxNhyIvVhOaQOMJgFqFVDbpEfGqRqlh8S6kFVcD7UKeGDRMADAz5klCo+IMeYpOBB7MTHduWRiMgBgw2Fee/REJpOAw8X1SI0NxfRB0UjuE8TpacaYGQdiLyamO2+ePQhRIf749XA5jCaunvY0eVWNaNQZkZYcAZVKhfPHJKJRZ+QCO8YYAA7EXu1QUR3CAnyRGhuKc0bGo0rbin0FtUoPi3WSVVwHABiTFAEAuCCtLwCunmaMEQ7EXkrTokdeVSNGJ0VArVbhvLREAMAvWfzm7mky2zIXacmRAIDRSeHoHxWMP45wepoxxoHYax0uboAgAGP60Sxr+qBohAf6YmN2GUycnvYoWUX18FGrMDIxHADM6ekmnRF/HqtQeHSMMaVxIPZSYqHW2LZZlr+vGmePTEBpfQsOWuxZZcoyGE3ILmnAkLhQBPn7mO8/vy2D8ROnpxnr9TgQe6nM4rZ0Z9u6IwCcNzoBAPDrYW7u4SlOVjaiWW/EmOSIDveP6huOgdHB2HSkgg+CYKyX40DspTKL6hAV4o/kPkHm+2YNiUFogC9+ySrl3tMeIqu44/qwSExPN+uN2MLpacZ6NQ7EXqimUYfTNc1IS6LtMKJAPx8sGB6HotpmZJc0KDhCJspqWyawzFyIzufqaY/V2GrgQjrmNhyIvZA4yxqbfOabu5ie5uppz5BZXA9ftQrDE8LO+NyIxDAMignBpqPlaNIZFBgds8ZoErDoP9tw69p9Sg+F9RIciL1QZttBD53TnQAwb1gcgvx8sOFwGaenFaY3mpBT0oBhCWEI9PM54/MqlQoXjElEi96ETUc4Pe0pMovqUFTbjC3HKlFa36z0cFgvwIHYC2V2MyMO8vfBvGGxOFXViGNtB0IwZZwo16LVYDqjUMvS+WM4Pe1pLM+L5p8LcwcOxF4os6gOCeGBiAsPtPp5sbkHH42oLLGjVlpSZJePGRofisFxodhyrALaVk5Pe4LtuVVQqQAftQo/ciBmbmAzEBuNRjzyyCO4+uqrcc0116CwsBAFBQVYunQpli1bhpUrV8Jk4uP33KW8oQXlDa1I62aWtWB4HPx91XwIhMLEjlrdzYhVKhXOT0tEq8GETUfK3TU01oXGVgMOFNYiLSkCM1Kjceh0HQqrm5QeFuvhbAbiLVu2AAA+//xz/POf/8SqVauwatUq3H333Vi/fj0EQcCmTZtkHygj4pu7tbS0KDTAF3OGxOJ4uRYnK7XuGhrrJKu4Hv4+agyNP7NQy9L5YyiDwWlQ5e0+VQO9UcDMwTG4sG3Z4KcsPrLS3TQtetQ365UehtvYDMQLFy7Ev/71LwBASUkJYmJikJ2djSlTpgAA5syZg/T0dHlHyczEjlrWCrUscXMPZekMJhwt1WBEYhj8fbv/MxsaH4ah8aH483glNC29583HE21vWx+ePTgGi0YlwM9HhR8P8QWSOx0r02D+S1sx78UtyC6pV3o4bmHXGrGvry8eeugh/Otf/8KiRYsgCIJ5/2pISAg0Gi4KchdzutPKvlRLC0fEw89HxduYFHK8XAOd0YTRNn5OovPT+kJn4Opppe3IrUSgnxoTB/ZBRLAf5gyJxZHSBuRWcGbJHQ4X1+Pqd3ehStuKumY9lr2XYZ589GR2F2s9//zz2LhxI5544gm0traa729sbER4eLgsg2MdCYKAzKI69I8KRp8Q/24fGxHshxmpMcguaeA1LgXYsz5s6fwxlMHg3tPKKW9owfFyLaakRCPAl7abXTi2LT2dyelpue0vrMXS9/5CXbMeL1w+Bi9fMRaaFj2ueS8D+wt79vGuNgPxd999h3feeQcAEBQUBJVKhdGjRyMjIwMAsG3bNkyaNEneUTIAQFFtM2qb9N0WallanEZv7ly05X72VExbGhwXhuEJYdh2vBINnJ5WxM7c9rS0aOHIeAT4qvHjoRKP3Zd/uLgeOoN3F8xm5FVj+fsZaNIZ8Z+rxuHKyf1w2YRkvHrVODTpjVj+fgb25NcoPUzZ2AzE55xzDnJycnDNNdfgxhtvxKOPPoonn3wSq1evxlVXXQW9Xo9Fixa5Y6y9nj2FWpbOHpkAH7UKG3id2O0yi+oR4KvGkPhQu59zfloidEYT/sjh6mkliPuHZ1oE4tAAXywYHoeTlY04WuZ5S3A/HirBBat34KXfjik9FKdtP1GJFR/ths5owpvLxuPicUnmz108LglvLB2PVoMJ132wG7tOVjv9dTQteuwrqPXICypfWw8IDg7Ga6+9dsb9a9eulWVArGvmQi07Z1lRIf6YNigKO3OrUVLXjL6RQbafxFzWojfiWJkGackR8POxf6v+4jGJePn34/g5sxSXTUiWcYSsM0EQsCO3CjGh/me0I71wbF9sOFyGHw+VYESi5yzDNekMePbnIwCAtX8V4LZ5qYgM7n7JytP8kVOO29btB1TAu8snYf7wuDMec15aItb4qHHbun342393473rJmH2kFi7v0azzoiPd+Xj7a0nUdekx2OLR+DmOYOk/DZcxg09vEhmUT1UKmB0kv1vBueOpq0xXD3tPkfLNDCYBJsFdZ2lxoZiRGI4tp2o7FVbNzzB8XItKjStmDk4Bmq1qsPn5g+LQ7C/D37K9KxTzd7achJlDS1IiQlBk86IT3YVKD0kh/ySVYpb1u6Dj1qFj66fbDUIi84eGY93l0+CSQBu/HivXSeWtRqM+O/OU5j9whY8t+EoTCYBUSH+eO7Xoy7NrOXAgdhLmEwCDhfXY1BMCMIC/ex+3qJR8VCpeJ3YnbLs3GJmzQVjEqE3Cvid09NutaNtfXiWRVpaFOTvg7NHxqOwpsm8PKS0wuomvLs9DwnhgfjiH9MQHuiL/6bne83Z1t8eKMId6/cj0M8HH98wpcNyQFfmD4/DBysmQQXgH5/s6/JvxGA04Ys9hVjw0lY89WMOmnUG3LlgMLY/tADvLp8IFYA71u/3qD7iHIi9RF5VIzStBox18M09LiwQkwdEYW9BLSoaWuQZHOtAPB3L3oppS4vTxOYeXKXrTjtOVAKgM72tMTf38JCfyzM/50BnMOGRxcMRFxaIFTMGoqZRh//tPa300Gz6fHch7v3fIYQG+GLtTVMxJSXK7ufOHhKLj/42GT5qFW5duw8bLLZnmkwCvj9YjIWvbMVDX2ehUtuKm2alYNuD83HfOcMQEeSHSQOj8Pj5I1DdqMNt6/aj1eAZFy4ciL2EWIXrzJv7eWkJEARgYzanp90hs6geQX4+SI21v1BLlBITglF9w7H9RBXqmzg97Q46gwkZp2qQGhuCxAjrdRSzh8YgLNAXP2WWwmRSNj29/UQlfsspx+SBfXBR2/aq62cMRKCfGu9uy4Pe6LkV1B+n5+Phb7LQJ9gfn/19Gsb1i3T4NWakxuDjG6YgwFeNOz47gB8OlWBjdhnOe2077vr8IIpqm3HN1P7Y9sB8PH7BSESHBnR4/ooZA3HxuL44UFiHZ346ItF35hoOxF7i0GmaZTmT7jx3tLiNiQOx3Jp1Rpyo0GJU33D4dFprtNcFY/rCYBKwMYd/Xu6wv7AWTTpjtwVAAb4+OHdUAkrrWxTd06o3mvD0jzlQq4CnLhplbqwUHRqAKyf1Q3Fds8e2Sv10Vz5W/pCN2LAAfP73aRjV1/FJhWhKShQ+uXEqgv188M/PDuAfn+7DiQoNLp+QjC33z8Ozl6YhIcL6oTgqlQqrLkvD8IQwfPpXAb7eV+T0OKTCgdhLZBXXw0etwqi+jldtJkYEYXz/SPyVV41qbavtJzCn5ZQ2wGgS7N7rbc35benpr/Yq/wbRG4jblqytD1u6oG32+eMh5dLTn+wqQG6FFldP6X9GILt59iD4qFVY8+dJjyoqA2gWv/KHbMSEBuB//5hus/+6PSYO6IO1N03FgOhgnJ+WiN/umYOXrxyLflHBNp8b7O+Lt6+diLBAXzz6bZbirTQ5EHsBg9GE7JJ6DI23fsC8Pc4bnQCTAC4CkplYqOXMEoKof3Qw5g6Nxe78Ghw6XSfNwLzML1mleH97HjZmlyGnpEHWHtw7cqvgo1ZhWmp0t4+bkRqNqBB//JxVBqMC6ekqbSv+8/txhAf64v5zhp3x+X5RwbhwTCKOlWvsqip2l7xKLW5ftx++ajXeWT4RKTEhkr322H6R2PrAfLx5zQQMjnMsuA+MCcF/rhqHVoMJt6zdh7omnWTjcpTNfcRMecfLtWjRm+xu5GHNeaMT8e9fjuLbA8W4anI/c0qLSSuzrVDL3r3eXbl59iBsPV6J97bn4Y1lEyQYmffIrdDQ3tJOIoL80C8qCP36BKNfVDCS+9DHqbGh6B9texZkTX2THplFdZjQvw9CA7p/O/TzUePc0QlYn1GIjLxqzLCj0ldKL208Bk2rAU9fNApRXbS4vWVeKr47WII1f57EguHxbh2fNfVNetz08V40tBjw8hVjMXFAH6WH1MFZI+LxzwWD8frmXNz9xUF8uGLyGdvX3IEDsRdoL9SKdPo1+kUFY0ZqNNJPVuOV34/jPitX1Mx1WUX1CPH3wSAXr/pnDo7G8IQwbDhchqLaJiT3cS7QeKMPduQDAO5cMBihAb4oqm3G6domnK5pwolyLQ4XN5zxnBcuH4MrJ/dz+GvtyquCSei6WrqzC8f0xfqMQvyYWeLWQJxZVIcv9p7GsPgwXDO1f5ePG54QjvnDYrHlWCX25tdg0kD7K5KlZjCacMdn+5FX1Yh/zB2Eyyd6ZpOauxYOxcGievx5rBKvbTqBe84e6vYxcCD2AoccPECgK68vHY/L16Rj9eZcxIcH4tppA6QYHmvT2GpAbqUWUwZGuXxVrVKpcPPsQbjvy0P4aGc+nrhgpESj9GzV2lZ8s78I/aKCcPfCoWcUvAmCgEptK07XNKOotglFtc14a0suXth4FIvHJNqc1XZmPvbQzkA8JSUKcWEB2HC4DP938WiHOqc5SxAEPPVDNgQBWHnhSPja+Jq3zhuMLccq8fbWk3hfwUD87C9HsP1EFRYMj8ODi4YrNg5bfNQqvH71OFywegde23QCY/tFuD2bwGvEXiCrqB7+vrYPmLclJjQAH/9tCqJD/PHk94d5O5PEckobIAiuXzCJLhzbF/HhAfhiz+lecxDEuoxCtBpMuGFmitWqc5VKhbiwQEwc0AcXj0vC7fMH4+9zUlGl1eG9bXkOf70duVUIC/C1e3++j1qFxWmJqGvSm5uAyO27g8XYX1iHxWkJds3CJw/sg4kD+uCPIxU4plB/7M93F+KjnfkYEheK164e5/QOAneJDPbH29dORICvGnd/fhAF1Y1u/fociD1cq8GIo2UNGJEYbvOAeXsMjAnBh9dPRoAvlf3vK+i5J5q4m9h1yd4ziG3x91VjxYyB0LYa8PnuQkle05O1GqhNY1iAL66YZH+a+abZKYgJDcB72/NQqbF/V8DpmiYUVDdhWmq0zVmmpQvdWD2tbTVg1S9HEeCrxqOLR9j1HJVKhVvnpgIA3tl2Us7hWZWRV40nvj+MPsF++GDFZIc6ASppdFIEnrlkNBpaDLhl7X63dinjQOzhjpZqoDcKLhVqdTa2XyTeunYCDCYBN368lw89l0h7xXSkZK95zZQBCPb3wUc78z26UYMUfjhYgiptK5ZO7e9QijkkwBd3LRyCJp0Rr286Yffzumtr2Z0J/SORFBmE37PL0aKX9836jc25qNC04pa5qQ7VCSwYHochcaH44WAJimrddx756Zom3LpuPwQBeOuaiU4X0Snlikn9cM3U/jhS2oCnf8x229flQOzh2k9cki4QA9TIftVlaahr0mPFh7tRzu0vXZZZXI+wQF8MsGMfo70igv1w5aR+KK1v8dhGDVIQBAEf7DgFH7UKK2YMdPj5V0/uh5SYEHy2uxCnquxLK5r3D9u5PixSqVS4YEwiNK0GbD1e6fBY7XWqqhEf7jiFpMgg3NI2w7WXWq3CLXNTYTAJeH/7KZlG2JG21YCbPt6LmkYd/u/i0ZhuYzuYp3rywpGYPiga+W5MT/eaQGwyCdh8tByHi6XduJ1VVI9PduXLtq9QLNQa60QrOFuunNQP9509FMV1zbj+oz2y7tXs6TQteuRVNiItKULy7Q83zEyBWgW8tz3P4xo1SGVnbjWOlmmwOC0RSU4c1+nno8YDi4bBYBLw0kbbZ/MaTQJ2nqxC34hApyrcLzD3npbv4uiZn3KgM5rw2PkjEOTveP+Ai8b1Rd+IQHyx5zRqGuXdI2s0Cbj78wM4Vq7BiukDsKybym5PF+Drg3U3TcVnN09z29fs8YFYbzThq31FOPvVrbjhv3tx6Vs78ZlE623f7C/C5WvS8eT32fg5S54/yKyiegT7O9e32B53LBiMZW2pmFvW7oPO0LPTn3IRt9S40lGrK/2jg3Hu6ARklzRgV55nHd8mlQ92UKHVjbNSnH6N80YnYGy/SPycVYqDNhqhZJfUo65Jj1lDYpzaUz86KRwDo4PxR045mnQGJ0fctS3HKrDpaAWmDYrCeW0tah3l56PGTbMHoVlvxMfp+dIOsJOXfjuGP45UYNbgmB5R4a9Wq9zaa6HHBuIWvRGf7MrHvBf/xP1fHkJBdRMuGdcXoQG+eOSbLDz5/WGn19yMJgHPbTiKe/93CAF+aqhVwFtbciWfrTTpDDhRocHovhGyVR2qVCr830WjsHBEPHbmVuPBrw4p3tTeG5n3ervYyKMrN82mg8zdlWZ0p9wKDbYcq8SkAX2cOgRApFKp8Mh5tE3muQ1Huv17NK8PO3DAfOevdeHYvmjWG7H5qLRdrOqb9Xjqh+wz+kk74+op/RAZ7IePd+XLcsEA0JGGa/48iZSYELy5bIJDhW+M9Lj/Y9pWA97eehKznt+CJ7/PRnVjK66fMRBbH5yP/1w9Hj/cMQvDE8Lwya4CXPfBbodTNtpWA/7x6V68vZV+8b67fSYuHNsXR8ukbyuXXdIAk4TbYbri66PG6qXjMaF/JL47WILnNx6V9ev1RJkS7fXuyoT+tCVl89EK5FYosyXFmmadETklDS5dhIoNPG6a7fxsWDRtUDQWDI/DX3k1+LOb9VtxfXiGC+uYYnpayuppMcVbUN2Ev89JxfAEx3vLWwr298X1MwairkmPz3dLd0Rii96IDVmluHXtPjz4VSbCAn3x3nWTEBHsHRXSnqbHBOLaRh1e+f04ZqzahOc2HEWr3ojb5qVix0ML8NRFo8zrTv2igvH1rTOwaFQ8duVV4+I3d+Bo2Zmdeqw5XdOEJWvS8ceRCswcHI1vb5uB1NhQ3DqPCine2CztrFjsMyxHurOzIH8ffLBiMgbFhuCdrXn4aGfPm3nJ6XBxPSKD/ZDcx/H1TXvd3BaoPtih3M9GZzBhT34N/vPHcVz5zi6MeXojFr++Hc9tcO7iraZRZ27gcfZI51KwnT107nCoVMDzG45ard1o1hmxN78WIxPDEdPpiDxHDEsIw9D4UGw5VilZfcVLvx3DlmOVmD0kBg8skqb73YrpAxHk54P3t+e5tPRkMJqw9Xgl7vvfIUx65g/cum4/NhwuQ/+oYLy7fBIGx8mzfNYbeH1nrfomPVZvPoH1uwvRpDMiKsQfDywahmunDUBEkPWrs5AAX6y5ZiJe23QCr206gcveSserV43DolFdvxHsya/BLZ/uQ3WjDsunDcCTF440d9UZnhCOhSPi8ceRcmScqsG0QdJUC4qzLHubDbiqT4g/Pv7bFFy2Jh3/91MOKjStuG76gC7PaHWUttUAg9GEyGDrfXLlVt+sR6vBiLgw68ejOf26TXrkVzdhtpPrjfY6e2QCBkQH4+v9xbjvnGEuBRF7GU0CckoakH6yCjtPVmPPqRo0t23ZUamA0X0jUNukwzvb8jA4LtSh/b8AsO6vArQaTPjbDOsNPJwxLCEMl09Ixlf7ivDdgeIzWivuzq+Bzmiyu5tWdy4Y0xev/H4cH6fn444FQ1x6rR8OUY/ogdHBeGPpBMn+f/QJ8cfVU/rho535+OFQCZY40GrSZBKwr7AWPxwswS9ZpahuyyAmRQbhmmn9cdHYvhiZGM69613k9YH4rT9z8f6OU0gID8T95wzD1VP6Idjf9relVqtwz9lDMTwhDPf+7xD+8ek+3Hv2UNy5YPAZv1Rf7j2NR7/NgkkA/nXxKCyfPvCM17ttfir+OFKON7fkShaIs4rrER7oiwFu3IvXLyoYH10/GSs+3I01f57Eu9vycM7IeCyfPgDTB0U7/AenM9BV9HcHivH7kXLoDCYMiQvFlJQo839SBfruHDpdhxv+uwc6gwnf3j5T0qv3wyXiQQ/yZi581CrcMDMFK3/Ixqe7CmTtifvDoRL8nFmCv/JqUN/cPtsbEheKGanRmDE4BtNSohER7IdTVY245M2dePTbLAyIDsGUFPvaKrYajPi4rYGHM32iu3Pv2UPxw6ESvPL7cZw/JrHDqWU7c53btmTNlZP64ZNdBXjpt+NQqVS4ff5gp17ncHE9HvzqEEID5Enx3jR7ED7dVYA3Np9AXZMOPmoVfNUq+KjV8FWroDb/m/5Tq1Q4UFiLHw+VoKSetjZGh/jjuukDcNHYvpjQv48ihyP0VCrBy/dDVDS04MDpOswbFosAX+eOCMwpacDNn+xFcV0zFqcl4KUrxiLY37etKOsI3tt+ChFBfnjrmgmY2c3m/2Xv/YX0k9X44Y6ZLjd1qG/WY+zTv2HW4BisvWmqS6/ljGadET8cKsbH6QXIKaXU/ZC4UFw3YyAuHZ/UbcMFQRBw4HQdvjtQjB8PlaC2id7IB8eFIjEiEPsK6CB2Uf+oYArKAykwD4gOlvQKe8vRCty2bj9aDEYIAjAoNgTf3z5Tso4/a/48ied/PYq3r52Ac0cnSvKaXWnSGTB91Wb4qFVIf3iB08didufjdDrAHaCZz8zB0Zg5OAbTB0UjLtx6NiE9twrXfbgbYYG++P72WXY1cvhy72k88FUmbp6dgsfOl77SdtUvR/DOtjw8tngEbp4zyHz/ea9tx8lKLTJXniPJ/79TVY249v0MFNc149Z5qXhw0TCHfn+rtK24+I2dKK5rxnvXTcLZI+Xpc/zAl4fw5T7HzrgOC/DFotEJuGhsX8xwsAMZs5/XB2KpVGtbceu6/dh9qgYjEsPxn6vG4flfj2Lz0QoMig3BBysm2zxHc2duFa55PwPnjkrA28snujQe8bVum5eKB89VrmG6IAjYX1iLj9MLsOFwKfRGAaEBvlgyMRnXThvQYWZZWN2Ebw8U47uDxeamCjGh/rhobBIuHZ+E0UmUwtIbTcgpacDuUzXIOFWDPfkdZ11xYQGYkhKF66YPtHt21ZX/7TmNR77Ngq9ahdeXjsfe/Bq8t/0Uzh4Zj3eunSjJVf1t6/bhl6wy7Hx4gVN7YB31wq9H8dafJ/HspaNxzVRpD+7YmF2GW9buQ3RIANbeNMWhYqF1GQV47NvDGBIXim9um9HthY4gCDjvte04UaHF1gfmyXK6VH2THrNf2AyVSoVtD8xHRLAfKjWtmPzsH5g5OBrrbpJun2hxXTOWv5+BvKpGLJ82AE9fNMqu3y2dwYRr38/A7vwa3Hf2UNx5lmvp7e406QzIOFUDvcEEo0mAURBgNAkwGC0+NgkwmQTojSb0i6JzseW42GMdcSC2oDOY8PSP2ViX0b7PePaQGLyxbEKX682WBEHAJW+l49DpOvx+zxwMceGQhrf+zMULvx5zyyzLXhWaFny++zTWZRSgvIF6+s4aHINZQ2Lwe0459hXUAgAC/dRYNCoBl4xPwuzBMTavok0mAcfKNdh9qsYcnKu09PpLp/TDw+eNsOv/vyVBEPD6ply8+sdxRAb74YMVkzBxQBQMRhOu+3A30k9W496zh+KfErzxzXp+MxUAPb7QLWtlFQ0tmPn8ZvTrE4w/7p0rWYpwf2Etlr77F3zUKnzx9+lOFQk+/WM2PtqZj3nDYvHBisldrnOKF5oXjEmU9bzld7aexKoNR3HL3FQ8fN5wfH+wGHd9fhAPnTvcXGQplUpNK677cDeOlDbgsvFJeGHJGJu/+49/l4W1fxVicVoC3lw2gddaeynOM1jw91Xj2UvT8K9LRiPY3wd/mzkQH10/2e4goFKpcHvbH/eara41W888LW6HiXTpdaQUFxaIf541BDseWoC3rpmAqSlR2JFbhec2HMX+wlrMGhyDl64Yi72Pn43Xrh6P+cPi7EplqdUqjEgMx4oZA/HmNROw57Gz8NUt0zE8IQyf7T6Nha9sxS9ZpXZXpBuMJjz6bRZe/eM4kvsE4etbZ2DiAJpZ+/qo8cayCUiKDMKrfxzHpiPlLv0/qW3Uoai2GWnJEW57E40LD8TF45KQV9Uo2R7W/KpG3PTxXuiNJry5bILTlfqPLR6BuUNj8eexSvz7lyNdPu797dTAQ9wfLZcVMwaib0QgPtp5CiV1zQ4fe+iI2LAAfH7zNIzvH4lvDhTj9vX70Wrouhf1+oxCrP2rEMMTwvDikrEchHsxnhF3wWQSnJppmEwCzn1tG05WNuLP++ehn5N9h2c+txmtBiP2POaeWZazjpVpkFlUh9lDYpEQIW01st5owrvb8vDaphPQGUw4e2Q8/nXx6G6/TpPOgDvXH8CmoxUY1TccH/1tstUq6cPF9bh8TTr8fdT4/o6ZGORk57I/j1Xg+o/24J8LBuPec6TZbmKPo2UNOPc/2zE1JQpf/GO6S69VrW3F5WvSkV/dhH9fmuZye8KGFj0ueysduRVarLosDUundHy93AoNFr6yDRMH9MHXt85w6WvZQ1yLvmJiMnbkVqFFb8S+x8+WrdiosdWAmz/Zi/ST1Zg9JAbvLJ94RgHpnvwaLHvvL4QG+OKHO2Y5/T7BegaeEXfB2T9StVqF2+YNhtEk4F0nzkcFKMVVXNeMMcmRHh2EAdoqcsWkfpIHYYBa9N0+fzB+vWs2pqZE4feccix8ZSs+3ZVvtftXtbYVS9/LwKajFZg9JAZf/GN6l1uVRidF4LnL06BpNeDvn+6DttXxrkNbjlXg/i8PAQAmufkA9uEJ4Zg9JAYZp2qQVeR8//QWvRE3fbIX+dVNuH1+qiQ9gsMDaSmgT7AfnvjuMHad7NiW88Od+QCAm1xoZ+mIyyYkY1h8GL7cV4TS+hbMGBwja8VvSIAvPrx+MhaOiMP2E1W47oPdHWogSuqacevafTAJwJvLJnAQZhyI5XDBmET0iwrCF3tPo0Lj2KlGja0G/POzAwCAqS4WKvUUg2JD8dnN0/DcZWlQqYAnvs/GFe/swony9g5TBdWNuHwNrc9fNiEJH6yYbPMovUvHJ+OGmSnIrdDivv8dtLu1Z4veiJXfH8bfPtqDhmYDHj9/hCypTltubkvrvrfduQs+o0nAXZ8fwIHCOlw6Pgn3SzijHxAdgrevnQiVCrh13T7ktxXv1TTq8PU+auBxTjf79qXko1bhofPav7fZDh576IxAPx+suXYiLhrbF3sLarHsvb9QrW1Fi96Iv3+6F1VaHZ44fwRmuGEszPNxIJaBr48at8xNhc5gcqgLkqaFjiTclVeN80Yn4G8z3TNj8AZqtQpXT+mPTffOxeK0BOwrqMXi17fj1d+PY29+DS57K908q3v5irHw97XvV/uRxcMxbVAUNmaX27Wun1PSgAtX78DHuwowJC4U390+EzfNHqRI5mL2kBgMTwjDz1ml+D2n3KETwARBwL9+ysHG7HJMHxSN5y8fI/n3MHVQNJ65ZDTqmvS48eM9qG/Wmxt4XC9hAw97zB8Wh2mDouCjVmH2UOf6SzvKz0eNV68ah6VT+iO7pAFXvrMLd39+EIeLG3DlpGSnjntkPROvEcukRW/EnBe2oLHVgPSHz7K5Qb++SY/rPtqNQ6frcNHYvnjlyrG8Z68bv+eU44nvDqOs7RxltQp4+uLRWD7N8e08VdpWXLR6B0obWvDh9ZMxf1jcGY8xmQR8uPMUXvj1GHRGE1ZMH4BHFo9QfGvHL1mluG3dfgC053fplH64cnI/m93D3t+eh2d+PoKh8aH48pYZDlelO+LZn3Pw3vZTmD0kBkfLNGjRGZH+yALJ9nHbq75Zj9M1TRgtc+OVzgRBwKoNR81LVeP7R+Lzv09zuu8B63k4EMvovW15ePaXIza3ydQ26nDtBxnILmnAkonJeP7yMW6dLXgrTYseL248hl8Pl+Ffl4zutkWpLZlFdVjy9i4E+qrxwx2zMNBiz3h5Qwvu//IQtp+oQkyoP15cMhbzh58ZrJVyuLge6zIK8f3BYjTpjPBVq3DOqHhcM5W6oXVeD/05sxS3r9+P+PAAfHvbTPSVee+z0STg5k/2miu85Wrg4ckEQcB72/Ow5WglXrt6XJeNUVjvxIFYRo2tBsx4bjNUKmDnQwsQYmXNskrbimvfz8DRMg2WTumPZy8Zza3jFPL1viLc9+UhDI0Pxbe3zURIgC82Zpfh4a8zUdukx/xhsXhhyVjEhsnf49kZmhY9vjtYgnV/FeBoGa2fp8SEYNmU/lgyMRl9QvyxJ78G17yfAT+1Cv+7ZTpG9XXP7FDToseSNbtwqqoRm++fK0sDD8a8FQdimf3nj+P4zx8n8Pj5I87YM1ne0IJl7/2Fk5WNuH7GQKy8cKTHV0n3dE/9kI3/pufj3FEJ6BPij892FyLAV43Hzh+B5dMGeMXPh7qh1WFdRgF+zixFq8EEf181zhudgK3HK6FpMeCj6ydjjpvWSkWaFj0qNa1ObxVjrKfiQCyzuiYdZjy3GWGBvtj24HzzulBJXTOWvfcX8qub8Pc5g/DIecO94k2+p9MbTbjmPWo5CADDE8Lw+tLxGOpClzQl1TXp8PX+YqzLKEBeJVUuv7BkDK508JQkxph8OBC7wb9/OYJ3t+WZmxucrmnC0vf+QlFtM+6YPxj3nTOUg7AHqdS04s7P9mNsciTuPWdojyiqEQQBGW1HGForRmOMKYcDsRtUNLRg1vNbkBgZiA9WTMZ1H2SgpL5F9ibvjDHGPB8HYjd57NssrMsoRKCfGi16Ex45bzj+MVfapvOMMca8D29UdZN/zEmFj1qFFr0JKy8cyUGYMcYYAJ4Ru9Wvh8vg56PCWSPkOfibMcaY9+FAzBhjjCmIU9OMMcaYgjgQM8YYYwriQMwYY4wpiAMxY4wxpiAOxIwxxpiCOBAzxhhjCuJAzBhjjCmIAzFjjDGmIA7EjDHGmII4EDPGGGMK4kDMGGOMKYgDMWOMMaYgDsSMMcaYgjgQM8YYYwriQMwYY4wpiAMxY4wxpiAOxIwxxpiCOBAzxhhjCuJAzBhjjCmIAzFjjDGmIA7EjDHGmII4EDPGGGMK4kDMGGOMKchX6QF055tvvkFxcXGH+y699FIAwLffftvh/qSkJFx22WXdPi85OVnG0dqvJ35f/D15x/cE9IzvS47voaioqNvnKs3Z71lpvfFn5TDBg+Xn51u9r6v7bT3PU/TE74u/J+/4ngShZ3xfcnwPnvT9WePs96y03vizchSnphljjDEFcSBmjDHGFMSBmDHGGFMQB2LGGGNMQR5dNb1582ZotdoO9y1YsAAAsHr16g73h4aG4m9/+5vN53mCnvh98ffkHd8T0DO+Lzm+B61W2+1zlebs96y03vizcpRKEARB6UEwxhhjvRWnphljjDEFcSBmjDHGFMSBmDHGGFMQB2LGGGNMQRyIGWOMMQVxIGasB8rIyMDy5cuVHgZjzA4ciBljjDEFcSBmrAc7deoUli9fjgsvvBBXXXUVMjMzAQDFxcW47rrrcMEFF2DJkiU4evSowiNlrPfiQMxYD/bAAw9g+fLl+PHHH/HII4/grrvugk6nw9NPP41Fixbhp59+wp133ok1a9YoPVTGei2PbnHJGHNeY2MjioqKcM455wAAxo0bh4iICOTl5WHPnj145ZVXAABz587F3LlzlRwqY70az4gZ66Gsda8VBAFGoxG+vr4d7svNzXXn0BhjFjgQM9ZDhYaGIjk5Gb/99hsA4ODBg6iqqsKQIUMwadIk/PzzzwCA9PR0PPHEE0oOlbFejVPTjPVgL774Ip566imsXr0afn5+WL16Nfz9/fHkk0/i8ccfx/r16xEUFIRnnnlG6aEy1mvx6UuMMcaYgjg1zRhjjCmIAzFjjDGmIA7EjDHGmII4EDPGGGMK4kDMGGOMKYgDMWOMMaYgDsSMMcaYgv4fRL54+UFJdhsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graphdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplot의 함수를 사용해서도 그래프 출력 가능"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-01T09:59:35.155865Z",
     "iopub.status.busy": "2020-08-01T09:59:35.154862Z",
     "iopub.status.idle": "2020-08-01T09:59:35.161867Z",
     "shell.execute_reply": "2020-08-01T09:59:35.160873Z",
     "shell.execute_reply.started": "2020-08-01T09:59:35.155865Z"
    }
   },
   "source": [
    "<b> plt.subplots 사용해서 fig,ax를 만들고, 이 바탕에 그래프를 출력 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:23.536419Z",
     "iopub.status.busy": "2020-09-28T12:12:23.535403Z",
     "iopub.status.idle": "2020-09-28T12:12:23.699407Z",
     "shell.execute_reply": "2020-09-28T12:12:23.699407Z",
     "shell.execute_reply.started": "2020-09-28T12:12:23.536419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loc'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(ax=ax)  # default: kind='line'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:31.176418Z",
     "iopub.status.busy": "2020-09-28T12:12:31.176418Z",
     "iopub.status.idle": "2020-09-28T12:12:31.713426Z",
     "shell.execute_reply": "2020-09-28T12:12:31.712414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:31.176418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loc'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(kind='bar', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:32.386413Z",
     "iopub.status.busy": "2020-09-28T12:12:32.386413Z",
     "iopub.status.idle": "2020-09-28T12:12:32.893419Z",
     "shell.execute_reply": "2020-09-28T12:12:32.892407Z",
     "shell.execute_reply.started": "2020-09-28T12:12:32.386413Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loc'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(kind='bar', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:33.157417Z",
     "iopub.status.busy": "2020-09-28T12:12:33.157417Z",
     "iopub.status.idle": "2020-09-28T12:12:33.337403Z",
     "shell.execute_reply": "2020-09-28T12:12:33.337403Z",
     "shell.execute_reply.started": "2020-09-28T12:12:33.157417Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf['pm25'].plot(kind='hist', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:34.120414Z",
     "iopub.status.busy": "2020-09-28T12:12:34.120414Z",
     "iopub.status.idle": "2020-09-28T12:12:34.309404Z",
     "shell.execute_reply": "2020-09-28T12:12:34.308414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:34.120414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pm25', ylabel='pm10'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf.plot(kind='scatter', x='pm25', y='pm10', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:35.034414Z",
     "iopub.status.busy": "2020-09-28T12:12:35.033413Z",
     "iopub.status.idle": "2020-09-28T12:12:35.354405Z",
     "shell.execute_reply": "2020-09-28T12:12:35.352406Z",
     "shell.execute_reply.started": "2020-09-28T12:12:35.034414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='pm25'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf['pm25'].plot(kind='pie', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:36.559419Z",
     "iopub.status.busy": "2020-09-28T12:12:36.559419Z",
     "iopub.status.idle": "2020-09-28T12:12:37.166417Z",
     "shell.execute_reply": "2020-09-28T12:12:37.165407Z",
     "shell.execute_reply.started": "2020-09-28T12:12:36.559419Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='loc'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# without Hangul font-family settingkkk\n",
    "fig, ax = plt.subplots()\n",
    "graphdf['pm25'].plot(kind='bar', ax=ax)\n",
    "graphdf['pm10'].plot(kind='line', ax=ax, color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> 그래프이 개별 항목 조절이 가능하다. 아래는 matplotlib 그래프의 주요 항목 이름 <br>\n",
    "    (From Practical Business Python by  Chris Moffitt)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://pbpython.com/images/matplotlib-anatomy.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip: </b> 그래프를 출력하는 방법이 여러가지가 있음. 외부 article을 보아도 조금씩 다른 방식을 사용함. 개인적으로 pandas보다는 matplot 방식을 추천</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 그래프에서 한글 깨짐을 방지 위한 한글 font 등록 방법\n",
    "(아래 부분도 일단 외워두고, 한글이 깨지는 상황에는 무조건 적으로 사용!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> step 1</b>: 먼저, 현재 시스템에 등록된 한글 폰트를 확인. <= 사용할 수 있는 폰트 목록 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:43.020418Z",
     "iopub.status.busy": "2020-09-28T12:12:43.020418Z",
     "iopub.status.idle": "2020-09-28T12:12:43.227419Z",
     "shell.execute_reply": "2020-09-28T12:12:43.227419Z",
     "shell.execute_reply.started": "2020-09-28T12:12:43.020418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C:\\\\Windows\\\\Fonts\\\\PERB____.TTF',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\CALIFI.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\NIAGENG.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\SHOWG.TTF',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\GILC____.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\HTOWERTI.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\seguihis.ttf',\n",
       " 'C:\\\\WINDOWS\\\\Fonts\\\\ERASDEMI.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\GOUDOS.TTF',\n",
       " 'C:\\\\Windows\\\\Fonts\\\\ENBRUSH.TTF']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.font_manager as fm\n",
    "font_list = fm.findSystemFonts(fontpaths = None, fontext = 'ttf')\n",
    "font_list[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> step 2</b>: 폰트 경로를 통해, font family name을 확보 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:44.866402Z",
     "iopub.status.busy": "2020-09-28T12:12:44.866402Z",
     "iopub.status.idle": "2020-09-28T12:12:44.891412Z",
     "shell.execute_reply": "2020-09-28T12:12:44.890414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:44.866402Z"
    }
   },
   "outputs": [],
   "source": [
    "font_name = fm.FontProperties(fname='C:\\\\WINDOWS\\\\Fonts\\\\HYWULM.TTF').get_name()\n",
    "plt.rc('font', family=font_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<b>step 3 </b>: (방법 1) 현재 노트북 환경에 '나눔고딕' 한글 폰트를 등록. 새로운 노트북에 매번 동일 내용을 추가해야함\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:46.279407Z",
     "iopub.status.busy": "2020-09-28T12:12:46.279407Z",
     "iopub.status.idle": "2020-09-28T12:12:46.284417Z",
     "shell.execute_reply": "2020-09-28T12:12:46.283407Z",
     "shell.execute_reply.started": "2020-09-28T12:12:46.279407Z"
    }
   },
   "outputs": [],
   "source": [
    "plt.rc('font', family='NanumGothic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<b> step 3 </b>: (방법 2) matplotlib의 설정 파일 (matplotlibrc 파일)에 한글 폰트를 등록하는 방법. <br>\n",
    "한번만 등록하면 새로운 노트북에서도 자동으로 설정이 적용됨 <br><br>\n",
    "\n",
    "<pre>\n",
    "matplotlibrc 파일에서 아래와 같이 내용을 변경\n",
    "\n",
    "font.family : sans-serif  => font.family : NanumGothic\n",
    "axes.unicode_minus: True  => axes.unicode_minus: False  \n",
    "</pre>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "한글 설정에 대한 상세/추가적인 내용은 https://m.blog.naver.com/itisik/221789012960 글 참조"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplot의 함수를 사용해서도 여러 개의 그래프(ax)를 하나의 fig에 출력"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> plt 모듀의 subplots을 이용해서 fig, ax를 먼저 선언한 후 그래프 출력 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:49.368403Z",
     "iopub.status.busy": "2020-09-28T12:12:49.367411Z",
     "iopub.status.idle": "2020-09-28T12:12:50.189414Z",
     "shell.execute_reply": "2020-09-28T12:12:50.189414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:49.368403Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'PM10')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the canvas frame\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(14,5))\n",
    "# set the figure title\n",
    "fig.suptitle('Air Pollution in Seoul City', fontsize=14, fontweight='bold')\n",
    "# left (1st) sub-figure\n",
    "graphdf['pm25'].plot(kind='barh', ax=ax0)\n",
    "ax0.set(title='PM2.5')\n",
    "# right (2nd) sub-figure\n",
    "graphdf['pm10'].plot(kind='barh', ax=ax1)\n",
    "ax1.set(title='PM10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://pbpython.com/images/matplotlib-pbpython-example-header.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 그 외"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 그래프를 표시하는 테마 자체를 설정 가능. 아래는, R에서 사용되는 ggplot 테마로 바꾸는 방법 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:53.366414Z",
     "iopub.status.busy": "2020-09-28T12:12:53.365413Z",
     "iopub.status.idle": "2020-09-28T12:12:53.370403Z",
     "shell.execute_reply": "2020-09-28T12:12:53.369414Z",
     "shell.execute_reply.started": "2020-09-28T12:12:53.366414Z"
    }
   },
   "outputs": [],
   "source": [
    "# set the matplotlib default drawing style\n",
    "plt.style.use('ggplot')                   # plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:54.185404Z",
     "iopub.status.busy": "2020-09-28T12:12:54.185404Z",
     "iopub.status.idle": "2020-09-28T12:12:54.924413Z",
     "shell.execute_reply": "2020-09-28T12:12:54.924413Z",
     "shell.execute_reply.started": "2020-09-28T12:12:54.185404Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'PM10')]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# prepare the canvas frame\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=1, ncols=2, figsize=(14,5))\n",
    "# set the figure title\n",
    "fig.suptitle('Air Pollution in Seoul City', fontsize=14, fontweight='bold')\n",
    "# left (1st) sub-figure\n",
    "graphdf['pm25'].plot(kind='barh', ax=ax0)\n",
    "ax0.set(title='PM2.5')\n",
    "# right (2nd) sub-figure\n",
    "graphdf['pm10'].plot(kind='barh', ax=ax1)\n",
    "ax1.set(title='PM10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b> 그래프를 화면에 표시하는 것 외에도, 이미지 파일로 저장 가능. 이미지 타입은 파일 확장자로부터 자동 선택 </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:56.366404Z",
     "iopub.status.busy": "2020-09-28T12:12:56.366404Z",
     "iopub.status.idle": "2020-09-28T12:12:56.620420Z",
     "shell.execute_reply": "2020-09-28T12:12:56.619413Z",
     "shell.execute_reply.started": "2020-09-28T12:12:56.366404Z"
    }
   },
   "outputs": [],
   "source": [
    "#\n",
    "#  fig 작업을 수행\n",
    "#\n",
    "#\n",
    "# save the figure into file\n",
    "fig.savefig('piechart.png', dpi=125)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:12:57.497414Z",
     "iopub.status.busy": "2020-09-28T12:12:57.497414Z",
     "iopub.status.idle": "2020-09-28T12:12:57.878405Z",
     "shell.execute_reply": "2020-09-28T12:12:57.876406Z",
     "shell.execute_reply.started": "2020-09-28T12:12:57.497414Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active code page: 65001\n",
      " Volume in drive D is D30G\n",
      " Volume Serial Number is 701B-706E\n",
      "\n",
      " Directory of D:\\jupyternotebook\\python_analytics_atoz\n",
      "\n",
      "2020-09-28  �삤�썑 09:12    <DIR>          .\n",
      "2020-09-28  �삤�썑 09:12    <DIR>          ..\n",
      "2020-09-28  �삤�썑 09:10    <DIR>          .ipynb_checkpoints\n",
      "2020-09-28  �삤�썑 08:56            46,474 1) jupyter_python.ipynb\n",
      "2020-09-28  �삤�썑 08:56           691,563 10) Wrapup.ipynb\n",
      "2020-09-28  �삤�썑 09:10         1,685,408 2) basic_pandas.ipynb\n",
      "2020-09-28  �삤�썑 08:56           536,159 3) advanced_pandas.ipynb\n",
      "2020-09-28  �삤�썑 08:56         3,965,421 4) python_modules.ipynb\n",
      "2020-09-28  �삤�썑 08:56           395,408 5) text_analysis.ipynb\n",
      "2020-09-28  �삤�썑 08:56           164,599 6) financial_analysis.ipynb\n",
      "2020-09-28  �삤�썑 08:56         1,115,769 7) financial_analysis_v2.ipynb\n",
      "2020-09-28  �삤�썑 08:56         2,260,270 9) vehicle plate detection.ipynb\n",
      "2020-09-28  �삤�썑 08:56         7,707,490 clien_park_aug_5to10.csv\n",
      "2020-09-28  �삤�썑 08:56        19,401,062 clien_park_aug_5to10_with_nouns.csv\n",
      "2020-09-26  �삤�썑 09:45    <DIR>          dataset_characters\n",
      "2020-09-28  �삤�썑 08:56             8,413 etfitemdata.csv\n",
      "2020-09-28  �삤�썑 08:56         1,982,835 image_sample.jpg\n",
      "2020-09-26  �삤�썑 09:42    <DIR>          img\n",
      "2020-09-28  �삤�썑 08:56             5,052 korean_stopwords.txt\n",
      "2020-09-28  �삤�썑 08:56           453,344 krxstockdata.csv\n",
      "2020-09-28  �삤�썑 08:56        10,093,184 License_character_recognition_weight.h5\n",
      "2020-09-28  �삤�썑 08:56            84,578 MobileNets_character_recognition.json\n",
      "2020-09-28  �삤�썑 09:07               764 new_result.csv\n",
      "2020-09-28  �삤�썑 09:12            72,326 piechart.png\n",
      "2020-09-26  �삤�썑 09:42    <DIR>          Plate_examples\n",
      "2020-09-28  �삤�썑 08:56             1,886 README.md\n",
      "2020-09-28  �삤�썑 08:56         1,183,686 SentiWord_info.json\n",
      "2020-09-28  �삤�썑 08:56           902,377 seoul_air_polution.csv\n",
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   ],
   "source": [
    "!dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b> Tip:</b> 데이터에 대한 간략한 통계, 분포를 통한 EDA를 자동으로 report 해주는 모듈 </b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-03T11:02:30.205142Z",
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     "iopub.status.idle": "2020-08-03T11:03:23.815139Z",
     "shell.execute_reply": "2020-08-03T11:03:23.809142Z",
     "shell.execute_reply.started": "2020-08-03T11:02:30.205142Z"
    }
   },
   "source": [
    "!conda install -c conda-forge pandas-profiling  -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:13:01.216418Z",
     "iopub.status.busy": "2020-09-28T12:13:01.216418Z",
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     "shell.execute_reply.started": "2020-09-28T12:13:01.216418Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9b7f46059e34bdc818ff16d5bdaab4b",
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       "HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=25.0, style=ProgressStyle(descrip…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a6bdad76e90454bbb5adedb3dc2b029",
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       "HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, description='Render HTML', max=1.0, style=ProgressStyle(description_wi…"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
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       "HBox(children=(FloatProgress(value=0.0, description='Export report to file', max=1.0, style=ProgressStyle(desc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "report = airdf.profile_report()\n",
    "report.to_file(output_file=\"report.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:13:22.639404Z",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "webbrowser.open(\"report.html\", new=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:13:23.521404Z",
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     "shell.execute_reply.started": "2020-09-28T12:13:23.521404Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Active code page: 65001\n",
      "Requirement already satisfied: sweetviz in c:\\programdata\\anaconda3\\lib\\site-packages (1.0b5)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (1.5.2)\n",
      "Requirement already satisfied: jinja2>=2.11.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (2.11.2)\n",
      "Requirement already satisfied: numpy>=1.16.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (1.19.1)\n",
      "Requirement already satisfied: matplotlib>=3.1.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (3.3.1)\n",
      "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (1.0.5)\n",
      "Requirement already satisfied: tqdm>=4.43.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (4.49.0)\n",
      "Requirement already satisfied: importlib-resources>=1.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from sweetviz) (3.0.0)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in c:\\programdata\\anaconda3\\lib\\site-packages (from jinja2>=2.11.1->sweetviz) (1.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (0.10.0)\n",
      "Requirement already satisfied: certifi>=2020.06.20 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (2020.6.20)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (7.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (1.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (2.8.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib>=3.1.3->sweetviz) (2.4.7)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,>=0.25.3->sweetviz) (2020.1)\n",
      "Requirement already satisfied: zipp>=0.4; python_version < \"3.8\" in c:\\programdata\\anaconda3\\lib\\site-packages (from importlib-resources>=1.2.0->sweetviz) (3.1.0)\n",
      "Requirement already satisfied: six in c:\\programdata\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib>=3.1.3->sweetviz) (1.15.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:13:30.775407Z",
     "iopub.status.busy": "2020-09-28T12:13:30.775407Z",
     "iopub.status.idle": "2020-09-28T12:13:30.819403Z",
     "shell.execute_reply": "2020-09-28T12:13:30.819403Z",
     "shell.execute_reply.started": "2020-09-28T12:13:30.775407Z"
    }
   },
   "outputs": [],
   "source": [
    "import sweetviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:13:30.821407Z",
     "iopub.status.busy": "2020-09-28T12:13:30.820403Z",
     "iopub.status.idle": "2020-09-28T12:13:39.195405Z",
     "shell.execute_reply": "2020-09-28T12:13:39.193405Z",
     "shell.execute_reply.started": "2020-09-28T12:13:30.821407Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":FEATURES DONE:                    |█████████████████████| [100%]   00:06  -> (00:00 left)\n",
      ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Associations graph... DONE!\n",
      "Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: no browser will pop up, the report is saved in your notebook/colab files.\n"
     ]
    }
   ],
   "source": [
    "analysis_report = sweetviz.analyze(airdf);\n",
    "analysis_report.show_html();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-28T12:13:39.198404Z",
     "iopub.status.busy": "2020-09-28T12:13:39.197404Z",
     "iopub.status.idle": "2020-09-28T12:13:45.583405Z",
     "shell.execute_reply": "2020-09-28T12:13:45.582417Z",
     "shell.execute_reply.started": "2020-09-28T12:13:39.198404Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":FEATURES DONE:                    |█████████████████████| [100%]   00:04  -> (00:00 left)\n",
      ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Associations graph... DONE!\n",
      "Report SWEETVIZ_REPORT.html was generated! NOTEBOOK/COLAB USERS: no browser will pop up, the report is saved in your notebook/colab files.\n"
     ]
    }
   ],
   "source": [
    "analysis_report = sweetviz.compare(airdf[airdf['loc'] == '중구'], airdf[airdf['loc'] == '구로구'])\n",
    "analysis_report.show_html()"
   ]
  }
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